WorldWideScience

Sample records for artificial learning approaches

  1. Machine learning an artificial intelligence approach

    CERN Document Server

    Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

    1983-01-01

    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

  2. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  3. Artificial Intelligence and Second Language Learning: An Efficient Approach to Error Remediation

    Science.gov (United States)

    Dodigovic, Marina

    2007-01-01

    While theoretical approaches to error correction vary in the second language acquisition (SLA) literature, most sources agree that such correction is useful and leads to learning. While some point out the relevance of the communicative context in which the correction takes place, others stress the value of consciousness-raising. Trying to…

  4. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  5. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  6. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  7. Building Artificial Vision Systems with Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    LeCun, Yann [New York University

    2011-02-23

    Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

  8. Artificial intelligence: Learning to see and act

    Science.gov (United States)

    Schölkopf, Bernhard

    2015-02-01

    An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529

  9. A new approach to artificial neural networks.

    Science.gov (United States)

    Baptista Filho, B D; Cabral, E L; Soares, A J

    1998-01-01

    A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.

  10. 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.

  11. An Artificial Intelligence Approach to Transient Stability Assessment

    OpenAIRE

    Akella, Vijay Ahaskar; Khincha, HP; Kumar, Sreerama R

    1991-01-01

    An artificial intelligence approach to online transient stability assessment is briefly discussed, and some crucial requirements for this algorithm are identified. Solutions to these are proposed. Some new attributes are suggested so as to reflect machine dynamics and changes in the network. Also a new representative learning set algorithm has been developed.

  12. Visual feature learning in artificial grammar classification.

    Science.gov (United States)

    Chang, Grace Y; Knowlton, Barbara J

    2004-05-01

    The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the visual features of the exemplars. When a change in the font and case occurred between study and test, there was no effect on sensitivity to grammatical rules in classification judgments. However, such a change did virtually eliminate sensitivity to training frequencies of letter bigrams and trigrams (chunk strength) in classification judgments. Performance of a secondary task during study eliminated this font sensitivity and generally reduced the contribution of chunk strength knowledge. The results are consistent with the idea that perceptual fluency makes a contribution to artificial grammar judgments.

  13. Proactive learning for artificial cognitive systems

    Science.gov (United States)

    Lee, Soo-Young

    2010-04-01

    The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

  14. Artificial intelligence approaches to software engineering

    Science.gov (United States)

    Johannes, James D.; Macdonald, James R.

    1988-01-01

    Artificial intelligence approaches to software engineering are examined. The software development life cycle is a sequence of not so well-defined phases. Improved techniques for developing systems have been formulated over the past 15 years, but pressure continues to attempt to reduce current costs. Software development technology seems to be standing still. The primary objective of the knowledge-based approach to software development presented in this paper is to avoid problem areas that lead to schedule slippages, cost overruns, or software products that fall short of their desired goals. Identifying and resolving software problems early, often in the phase in which they first occur, has been shown to contribute significantly to reducing risks in software development. Software development is not a mechanical process but a basic human activity. It requires clear thinking, work, and rework to be successful. The artificial intelligence approaches to software engineering presented support the software development life cycle through the use of software development techniques and methodologies in terms of changing current practices and methods. These should be replaced by better techniques that that improve the process of of software development and the quality of the resulting products. The software development process can be structured into well-defined steps, of which the interfaces are standardized, supported and checked by automated procedures that provide error detection, production of the documentation and ultimately support the actual design of complex programs.

  15. Abstraction processes in artificial grammar learning.

    Science.gov (United States)

    Shanks, D R; Johnstone, T; Staggs, L

    1997-02-01

    Four experiments explored the extent the extent to which abstract knowledge may underlie subjects' performance when asked to judge the grammaticality of letter strings generated from an artificial grammar. In Experiment 1 and 2 subjects studied grammatical strings instantiated with one set of letters and were then tested on grammatical and ungrammatical strings formed either from the same or a changed letter-set. Even with a change of letter-set, subjects were found to be sensitive to a variety of violation of the grammar. In Experiments 3 and 4, the critical manipulation involved the way in which the training strings were studied: an incidental learning procedure was used for some subjects, and others engaged in an explicit code-breaking task to try to learn the rules of the grammar. When strings were generated from a biconditional (Experiment 4) but not from a standard finite-state grammar (Experiment 3), grammaticality judgements for test strings were independent of their surface similarity to specific studied strings. Overall, the results suggest that transfer in this simple memory task is mediated at least to some extent by abstract knowledge.

  16. 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.

  17. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  18. Does Artificial Tutoring Foster Inquiry Based Learning?

    Science.gov (United States)

    Schmoelz, Alexander; Swertz, Christian; Forstner, Alexandra; Barberi, Alessandro

    2014-01-01

    This contribution looks at the Intelligent Tutoring Interface for Technology Enhanced Learning, which integrates multistage-learning and inquiry-based learning in an adaptive e-learning system. Based on a common pedagogical ontology, adaptive e-learning systems can be enabled to recommend learning objects and activities, which follow inquiry-based…

  19. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  20. Solution to reinforcement learning problems with artificial potential field

    Institute of Scientific and Technical Information of China (English)

    XIE Li-juan; XIE Guang-rong; CHEN Huan-wen; LI Xiao-li

    2008-01-01

    A novel method was designed to solve reinforcement learning problems with artificial potential field. Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF), which was a very appropriate method to model a reinforcement learning problem. Secondly, a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept. The performance of this new method was tested by a gridworld problem named as key and door maze. The experimental results show that within 45 trials, good and deterministic policies are found in almost all simulations. In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution, the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning. Therefore, the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

  1. Semantics boosts syntax in artificial grammar learning tasks with recursion.

    Science.gov (United States)

    Fedor, Anna; Varga, Máté; Szathmáry, Eörs

    2012-05-01

    Center-embedded recursion (CER) in natural language is exemplified by sentences such as "The malt that the rat ate lay in the house." Parsing center-embedded structures is in the focus of attention because this could be one of the cognitive capacities that make humans distinct from all other animals. The ability to parse CER is usually tested by means of artificial grammar learning (AGL) tasks, during which participants have to infer the rule from a set of artificial sentences. One of the surprising results of previous AGL experiments is that learning CER is not as easy as had been thought. We hypothesized that because artificial sentences lack semantic content, semantics could help humans learn the syntax of center-embedded sentences. To test this, we composed sentences from 4 vocabularies of different degrees of semantic content due to 3 factors (familiarity, meaning of words, and semantic relationship between words). According to our results, these factors have no effect one by one but they make learning significantly faster when combined. This leads to the assumption that there were different mechanisms at work when CER was parsed in natural and in artificial languages. This finding questions the suitability of AGL tasks with artificial vocabularies for studying the learning and processing of linguistic CER.

  2. Machine Learning Optimization of Evolvable Artificial Cells

    DEFF Research Database (Denmark)

    Caschera, F.; Rasmussen, S.; Hanczyc, M.

    2011-01-01

    An evolvable artificial cell is a chemical or biological complex system assembled in laboratory. The system is rationally designed to show life-like properties. In order to achieve an optimal design for the emergence of minimal life, a high dimensional space of possible experimental combinations...

  3. The Relationship Between Artificial and Second Language Learning.

    Science.gov (United States)

    Ettlinger, Marc; Morgan-Short, Kara; Faretta-Stutenberg, Mandy; Wong, Patrick C M

    2016-05-01

    Artificial language learning (ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom.

  4. Knowledge representation an approach to artificial intelligence

    CERN Document Server

    Bench-Capon, TJM

    1990-01-01

    Although many texts exist offering an introduction to artificial intelligence (AI), this book is unique in that it places an emphasis on knowledge representation (KR) concepts. It includes small-scale implementations in PROLOG to illustrate the major KR paradigms and their developments.****back cover copy:**Knowledge representation is at the heart of the artificial intelligence enterprise: anyone writing a program which seeks to work by encoding and manipulating knowledge needs to pay attention to the scheme whereby he will represent the knowledge, and to be aware of the consequences of the ch

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

    OpenAIRE

    Atris Suyantohadi; Mochamad Hariadi; Mauridhi Hery Purnomo

    2010-01-01

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

  6. Artificial intelligence approach to legal reasoning

    International Nuclear Information System (INIS)

    For artificial intelligence, understanding the forms of human reasoning is a central goal. Legal reasoning is a form that makes a new set of demands on artificial intelligence methods. Most importantly, a computer program that reasons about legal problems must be able to distinguish between questions it is competent to answer and questions that human lawyers could seriously argue either way. In addition, a program for analyzing legal problems should be able to use both general legal rules and decisions in past cases; and it should be able to work with technical concepts that are only partly defined and subject to shifts of meaning. Each of these requirements has wider applications in artificial intelligence, beyond the legal domain. This dissertation presents a computational framework for legal reasoning, within which such requirements can be accommodated. The development of the framework draws significantly on the philosophy of law, in which the elucidation of legal reasoning is an important topic. A key element of the framework is the legal distinction between hard cases and clear cases. In legal writing, this distinction has been taken for granted more often than it has been explored. Here, some initial heuristics are proposed by which a program might make the distinction

  7. Artificial intelligence approach to accelerator control systems

    International Nuclear Information System (INIS)

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms

  8. "Memory foam" approach to unsupervised learning

    CERN Document Server

    Janson, Natalia B

    2011-01-01

    We propose an alternative approach to construct an artificial learning system, which naturally learns in an unsupervised manner. Its mathematical prototype is a dynamical system, which automatically shapes its vector field in response to the input signal. The vector field converges to a gradient of a multi-dimensional probability density distribution of the input process, taken with negative sign. The most probable patterns are represented by the stable fixed points, whose basins of attraction are formed automatically. The performance of this system is illustrated with musical signals.

  9. Numerical approaches for approximating technical effectiveness of artificial recharge structures

    OpenAIRE

    Neumann, I.; Barker, J; MacDonald, D; Gale, I.

    2004-01-01

    This report describes various numerical approaches to quantify technical effectiveness of low-technology artificial recharge structures as seen commonly in rural environment and communities in semi-arid developing countries. The described methodologies enable benefits of artificial recharge facilities, i.e. their ability to replenish the aquifer, to be approximated. Technical effectiveness of recharge facilities is thereby evaluated on three scales: On a recharge basin scale, the rate of i...

  10. Numerical approaches for approximating technical effectivessness of artificial recharge structures

    OpenAIRE

    Neumann, I.; Barker, J; MacDonald, D; Gale, I.

    2004-01-01

    This report describes various numerical approaches to quantify technical effectiveness of low-technology artificial recharge structures as seen commonly in rural environment and communities in semi-arid developing countries. The described methodologies enable benefits of artificial recharge facilities, i.e. their ability to replenish the aquifer, to be approximated. Technical effectiveness of recharge facilities is thereby evaluated on three scales: On a recharge basin scale, the rate of i...

  11. Artificial Immune System Approaches for Aerospace Applications

    Science.gov (United States)

    KrishnaKumar, Kalmanje; Koga, Dennis (Technical Monitor)

    2002-01-01

    Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

  12. Artificial grammar learning of melody is constrained by melodic inconsistency: Narmour's principles affect melodic learning.

    Science.gov (United States)

    Rohrmeier, Martin; Cross, Ian

    2013-01-01

    Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990) melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011), the identical finite-state grammar is employed having terminals (the alphabet) manipulated so that melodies generated systematically violated Narmour's principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints.

  13. Learning comunication strategies for distributed artificial intelligence

    Science.gov (United States)

    Kinney, Michael; Tsatsoulis, Costas

    1992-08-01

    We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

  14. 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

  15. Learning and geometry computational approaches

    CERN Document Server

    Smith, Carl

    1996-01-01

    The field of computational learning theory arose out of the desire to for­ mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of geo­ metric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ­ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the C...

  16. DIGITAL SIMULATIONS FOR IMPROVING EDUCATION: Learning Through Artificial Teaching Environments

    OpenAIRE

    Reviewed by Özlem OZAN

    2009-01-01

    DIGITAL SIMULATIONS FOR IMPROVING EDUCATION:Learning Through Artificial Teaching EnvironmentsGibson, David, Ed.D.; Information Science Reference, Hershey, PA,SBN-10: 1605663239, ISBN-13: 9781605663234, p.514 Jan 2009Reviewed byÖzlem OZANFaculty of Education, Eskişehir Osmangazi University,Eskisehir-TURKEYSimulations in education, both for children and adults,become popular with the development of computer technology, because they are fun and engaging and allow learners to internalize knowledg...

  17. Statistical learning of two artificial languages presented successively: How conscious?

    Directory of Open Access Journals (Sweden)

    Ana eFranco

    2011-09-01

    Full Text Available Statistical learning is assumed to occur automatically and implicitly, but little is known about the extent to which the representations acquired over training are available to conscious awareness. In this study, we focus whether the knowledge acquired in a statistical learning situation is conscious or not. Here, participants were first exposed to an artificial language presented auditorily. Immediately thereafter, they were exposed to a second artificial language. . Both languages were composed of the same corpus of syllables and differed only in the transitional probabilities between the latter. We first controlled that both languages were equally learnable (Experiment 1 and that participants could learn the two languages and differentiate between them (Experiment 2. In Experiment 3, we used an adaptation of the Process Dissociation Procedure (Jacoby, 1991 to explore whether knowledge of each language was consciously accessible and manipulable. Results suggest that statistical information can be used to parse and differentiate between two different artificial languages, and that the resulting representations are conscious.

  18. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  19. Artificial life extension. The epigenetic approach.

    Science.gov (United States)

    Kloeden, P E; Rössler, R; Rössler, O E

    1994-05-31

    An epigenetic approach starts out from the direct (rather than the underlying genetic) causes. An epigenetic approach to aging has little chance of succeeding before a minimum amount of knowledge has been accumulated on the "genetic programming" that is currently believed to underlie aging. Two recent advances, one empirical and one theoretical, jointly brighten the prospect. The empirical one is the discovery that melatonin functions as an aging-controlling hormone in mammals. In 1979, Dilman and co-workers isolated a biologically active pineal extract (epithalamin) in rats which, as they later showed, stimulates melatonin production. Pierpaoli and co-workers in 1987 directly administered melatonin to mice. Both groups observed a surprising 25-percent increase of life span in conjunction with a postponed senescence. A similar effect was also achieved with an engraftment of young pineal tissue into the thymus of old mice by Pierpaoli's group. Beneficial effects of epithalamin in humans were reported by Dilman's group. The second advance is a deductive evolution-theoretical approach to aging discovered in 1988. In populations living in a niche with a fixed carrying capacity, any individual is in the long run replaced by a single successor. It follows that, as the expected cumulative number of adult progeny of the same sex approaches unity as a function of life time of the progenitor, the latter's survivability must approach zero if the sum is to remain unity. A physiological prediction follows: a centralized physicochemical clock--like a sedimentation process--must exist somewhere in the organism controlling a secreted substance that reaches all cells. In this way, the pineal coacervates and the pineal's hormonal product melatonin were arrived at on an independent route again. While melatonin as a drug has been used on human volunteers for decades, its anti-aging effect has yet to be proved. Detailed hormone profiles in different age groups and under different life

  20. Artificial Grammar Learning of Melody Is Constrained by Melodic Inconsistency: Narmour's Principles Affect Melodic Learning

    OpenAIRE

    Martin Rohrmeier; Ian Cross

    2013-01-01

    Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990) melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011), the iden...

  1. Artificial life approach to color contrast manipulation

    Science.gov (United States)

    Oliver, William R.

    1999-02-01

    Contrast enhancement methods have a long history of use in image processing for forensics and have been used to effect in the evaluation patterned injury of the skin. Most contrast enhancement methods, however, were developed for the evaluation of greyscale images and involve the manipulation of one dimension of data at a time. Contrast enhancement in a three- or more dimensional space poses challenges to the implementation of histogram equalization and similar algorithms. A number of approaches to dealing with this problem have been suggested, including performing operations on each channel independently or by various color `explosion' methods. Our laboratory has been investigating dispersion- and diffusion-based methods by modeling changes in color space as biological processes. In short, we model the migration and dispersion of points in color space as migration and differentiation. In this model, biological differentiation signals are used for segmentation in color space (color quantization) and chemoattractant and diffusion models are used for swarming and dispersal. The results of this method are compared with more traditional methods. Implementation issues are discussed. Extensions to the use of reaction-diffusion equations for color-space segmentation are discussed.

  2. What artificial grammar learning reveals about the neurobiology of syntax.

    Science.gov (United States)

    Petersson, Karl-Magnus; Folia, Vasiliki; Hagoort, Peter

    2012-02-01

    In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial grammar learning paradigm in 32 healthy Dutch university students (natural language FMRI data were already acquired for these participants). We predicted that artificial syntax processing would engage the left inferior frontal region (BA 44/45) and that this activation would overlap with syntax-related variability observed in the natural language experiment. The main findings of this study show that the left inferior frontal region centered on BA 44/45 is active during artificial syntax processing of well-formed (grammatical) sequence independent of local subsequence familiarity. The same region is engaged to a greater extent when a syntactic violation is present and structural unification becomes difficult or impossible. The effects related to artificial syntax in the left inferior frontal region (BA 44/45) were essentially identical when we masked these with activity related to natural syntax in the same subjects. Finally, the medial temporal lobe was deactivated during this operation, consistent with the view that implicit processing does not rely on declarative memory mechanisms that engage the medial temporal lobe. In the context of recent FMRI findings, we raise the question whether Broca's region (or subregions) is specifically related to syntactic movement operations or the processing of hierarchically nested non-adjacent dependencies in the discussion section. We conclude that this is not the case. Instead, we argue that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner, independent of whether there are any

  3. Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    JinHyo Joseph Yun

    2016-08-01

    Full Text Available What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

  4. Artificial grammar learning of melody is constrained by melodic inconsistency: Narmour's principles affect melodic learning.

    Science.gov (United States)

    Rohrmeier, Martin; Cross, Ian

    2013-01-01

    Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990) melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011), the identical finite-state grammar is employed having terminals (the alphabet) manipulated so that melodies generated systematically violated Narmour's principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints. PMID:23874388

  5. Artificial grammar learning of melody is constrained by melodic inconsistency: Narmour's principles affect melodic learning.

    Directory of Open Access Journals (Sweden)

    Martin Rohrmeier

    Full Text Available Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990 melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011, the identical finite-state grammar is employed having terminals (the alphabet manipulated so that melodies generated systematically violated Narmour's principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints.

  6. An artificial boundary approach for short-ranged interactions

    Science.gov (United States)

    Jacobs, David M.

    2016-07-01

    Real physical systems are only understood, experimentally or theoretically, to a finite resolution so in their analysis there is generally an ignorance of possible short-range phenomena. It is also well-known that the boundary conditions of wavefunctions and fields can be used to model short-range interactions when those interactions are expected, a priori. Here, a real-space approach is described wherein an artificial boundary of ignorance is imposed to explicitly exclude from analysis the region of a system wherein short-distance effects may be obscure. The (artificial) boundary conditions encode those short-distance effects by parameterizing the possible UV completions of the wavefunction. Since measurable quantities, such as spectra and cross sections, must be independent of the position of the artificial boundary, the boundary conditions must evolve with the radius of the boundary in a particular way. As examples of this approach, an analysis is performed of the non-relativistic free particle, harmonic oscillator, and Coulomb potential, and some known results for point-like (contact) interactions are recovered, however from a novel perspective. Generically, observables differ from their canonical values and symmetries are anomalously broken compared to those of idealized models. Connections are made to well-studied physical systems, such as the binding of light nuclei and cold atomic systems. This method is arguably more physically transparent and mathematically easier to use than other techniques that require the regularization and renormalization of delta-function potentials, and may offer further generalizations of practical use.

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

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

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

  8. Control approach development for variable recruitment artificial muscles

    Science.gov (United States)

    Jenkins, Tyler E.; Chapman, Edward M.; Bryant, Matthew

    2016-04-01

    This study characterizes hybrid control approaches for the variable recruitment of fluidic artificial muscles with double acting (antagonistic) actuation. Fluidic artificial muscle actuators have been explored by researchers due to their natural compliance, high force-to-weight ratio, and low cost of fabrication. Previous studies have attempted to improve system efficiency of the actuators through variable recruitment, i.e. using discrete changes in the number of active actuators. While current variable recruitment research utilizes manual valve switching, this paper details the current development of an online variable recruitment control scheme. By continuously controlling applied pressure and discretely controlling the number of active actuators, operation in the lowest possible recruitment state is ensured and working fluid consumption is minimized. Results provide insight into switching control scheme effects on working fluids, fabrication material choices, actuator modeling, and controller development decisions.

  9. IMBALANCED DATA LEARNING APPROACHES REVIEW

    Directory of Open Access Journals (Sweden)

    Mohamed Bekkar

    2013-07-01

    Full Text Available The present work deals with a well-known problem in machine learning, that classes have generally skewed prior probabilities distribution. This situation of imbalanced data is a handicap when trying to identify the minority classes , usually more interesting one In real world applications. This paper is an attempt to list the different approachs proposed in scientific research to deal with the imbalanced data learning, as well a comparison between various applications cases performed on this subject.

  10. An artificial immune approach for optical image based vision inspection

    Institute of Scientific and Technical Information of China (English)

    Hong Zheng(郑宏); Nanfeng Xiao(肖南风); Jinhui Lan(蓝金辉)

    2003-01-01

    This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method.

  11. Two modes of transfer in artificial grammar learning.

    Science.gov (United States)

    Tunney, R J; Altmann, G T

    2001-05-01

    Participants can transfer grammatical knowledge acquired implicitly in 1 vocabulary to new sequences instantiated in both the same and a novel vocabulary. Two principal theories have been advanced to account for these effects. One suggests that sequential dependencies form the basis for cross-domain transfer (e.g., Z. Dienes, G. T. M. Altmann, & S. J. Gao, 1999). Another argues that a form of episodic memory known as abstract analogy is sufficient (e.g., L. R. Brooks & J. R. Vokey, 1991). Three experiments reveal the contributions of the 2. In Experiment 1 sequential dependencies form the only basis for transfer. Experiment 2 demonstrates that this process is impaired by a change in the distributional properties of the language. Experiment 3 demonstrates that abstract analogy of repetition structure is relatively immune to such a change. These findings inform theories of artificial grammar learning and the transfer of grammatical knowledge.

  12. Information theory and artificial grammar learning: inferring grammaticality from redundancy.

    Science.gov (United States)

    Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

    2016-03-01

    In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

  13. Fractal Globules: A New Approach to Artificial Molecular Machines

    Science.gov (United States)

    Avetisov, Vladik A.; Ivanov, Viktor A.; Meshkov, Dmitry A.; Nechaev, Sergei K.

    2014-01-01

    The over-damped relaxation of elastic networks constructed by contact maps of hierarchically folded fractal (crumpled) polymer globules was investigated in detail. It was found that the relaxation dynamics of an anisotropic fractal globule is very similar to the behavior of biological molecular machines like motor proteins. When it is perturbed, the system quickly relaxes to a low-dimensional manifold, M, with a large basin of attraction and then slowly approaches equilibrium, not escaping M. Taking these properties into account, it is suggested that fractal globules, even those made by synthetic polymers, are artificial molecular machines that can transform perturbations into directed quasimechanical motion along a defined path. PMID:25418305

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

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. PMID:27297046

  15. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Directory of Open Access Journals (Sweden)

    Graciliano Nicolás Marichal

    2016-04-01

    Full Text Available In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles, where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  16. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  17. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  18. 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.

  19. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-04-12

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  20. Acquisition and manipulation of mental structures : Investigations on artificial grammar learning and implicit sequence processing

    OpenAIRE

    Forkstam, Christian

    2010-01-01

    This thesis introduces repetitive artificial grammar learning as a paradigm in the investigation of sequential implicit learning, in particular as a model for language acquisition and processing. Implicit learning of sequential structure captures an essential cognitive processing capacity of interest from a larger cognitive neuroscience perspective. We investigate in this thesis the underlying neural processing architecture for implicit learning/acquisition to acquire and ...

  1. BOUNDARY ELEMENT ANALYSIS OF CONTACT PROBLEMS USING ARTIFICIAL BOUNDARY NODE APPROACH

    Institute of Scientific and Technical Information of China (English)

    Bahattin KANBER; Ibrahim H. GUZELBEY; Ahmet ERKLI

    2003-01-01

    An improved version of the regular boundary element method, the artificial boundary node approach, is derived. A simple contact algorithm is designed and implemented into the direct boundary element, regular boundary element and artificial boundary node approaches. The exisiting and derived approaches are tested using some case studies. The results of the artificial boundary node approach are compared with those of the existing boundary element program, the regular element approach, ANSYS and analytical solution whenever possible. The results show the effectiveness of the artificial boundary node approach for a wider range of boundary offsets.

  2. Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics

    CERN Document Server

    Aerts, Diederik; Sozzo, Sandro

    2011-01-01

    The mathematical formalism of quantum mechanics has been successfully employed in the last years to model situations in which the use of classical structures gives rise to problematical situations, and where typically quantum effects, such as 'contextuality' and 'entanglement', have been recognized. This 'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in particular, on the quantum models that have been elaborated to describe how concepts combine in cognitive science, and on the ensuing identification of a quantum structure in human thought. We point out that these results provide interesting insights toward the development of a unified theory for meaning and knowledge formalization and representation. Then, we analyze the technological aspects and implications of our approach, and a particular attention is devoted to the connections with symbolic artificial intelligence, quantum computation and robotics.

  3. An intercomparison of artificial intelligence approaches for polar scene identification

    Science.gov (United States)

    Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

    1993-01-01

    The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

  4. E-LEARNING EXPERIENCE WITH ARTIFICIAL INTELLIGENCE SUPPORTED SOFTWARE: An International Application on English Language Courses

    OpenAIRE

    Kose, Utku; Arslan, Ahmet

    2015-01-01

    Nowadays, artificial intelligence supported e-learning scenarios are widely employed by educational institutions in order to ensure better teaching and learning experiences along educational activities. In the context of performed scientific studies, positive results often encourage such institutions to apply their intelligent e-learning systems on different types of courses and report advantages of artificial intelligent in especially education field. It seems that the future of education w...

  5. The Wonder Approach to learning.

    Science.gov (United States)

    L'Ecuyer, Catherine

    2014-01-01

    Wonder, innate in the child, is an inner desire to learn that awaits reality in order to be awakened. Wonder is at the origin of reality-based consciousness, thus of learning. The scope of wonder, which occurs at a metaphysical level, is greater than that of curiosity. Unfortunate misinterpretations of neuroscience have led to false brain-based ideas in the field of education, all of these based on the scientifically wrong assumption that children's learning depends on an enriched environment. These beliefs have re-enforced the Behaviorist Approach to education and to parenting and have contributed to deadening our children's sense of wonder. We suggest wonder as the center of all motivation and action in the child. Wonder is what makes life genuinely personal. Beauty is what triggers wonder. Wonder attunes to beauty through sensitivity and is unfolded by secure attachment. When wonder, beauty, sensitivity and secure attachment are present, learning is meaningful. On the contrary, when there is no volitional dimension involved (no wonder), no end or meaning (no beauty) and no trusting predisposition (secure attachment), the rigid and limiting mechanical process of so-called learning through mere repetition become a deadening and alienating routine. This could be described as training, not as learning, because it does not contemplate the human being as a whole.

  6. Transfer learning approach for financial applications

    OpenAIRE

    Stamate, Cosmin; Magoulas, George D.; Thomas, Michael S.C.

    2015-01-01

    Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behav...

  7. Information theory and artificial grammar learning: inferring grammaticality from redundancy.

    Science.gov (United States)

    Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

    2016-03-01

    In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute). PMID:25828458

  8. Artificial grammar learning meets formal language theory: an overview

    Science.gov (United States)

    Fitch, W. Tecumseh; Friederici, Angela D.

    2012-01-01

    Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective. PMID:22688631

  9. The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

    Directory of Open Access Journals (Sweden)

    Peng Zhang

    2015-01-01

    Full Text Available Ebola virus disease (EVD distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

  10. Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

    Science.gov (United States)

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-01-01

    Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

  11. An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium

    CERN Document Server

    Ramos, A A

    2005-01-01

    A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of...

  12. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  13. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-01-01

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. PMID:27399696

  14. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2016-07-01

    Full Text Available Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  15. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-01-01

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. PMID:27399696

  16. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  17. Design and evaluation of two blended learning approaches: Lessons learned

    OpenAIRE

    Cheung, WS; Hew, KF

    2011-01-01

    In this paper, we share two blended learning approaches used at the National Institute of Education in Singapore. We have been using these two approaches in the last twelve years in many courses ranging from the diploma to graduate programs. For the first blended learning approach, we integrated one asynchronous communication tool with face to face tutorials, classroom discussions, and a reflection session. For the second blended learning approach, we integrated two asynchronous tools with fa...

  18. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  19. Artificially drained catchments - from monitoring studies towards management approaches

    NARCIS (Netherlands)

    Lennartz, B.; Tiemeyer, B.; Rooij, de G.H.; Dolezal, F.

    2010-01-01

    Artificial drainage is successfully used to improve the moisture conditions of agricultural soils, but there are drawbacks, such as nutrient export and wetland destruction. This Special Section: Artificial Drainage of the Vadose Zone Journal features eight papers comprising diverse subjects, includi

  20. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  1. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    Directory of Open Access Journals (Sweden)

    Terry Howell

    2010-02-01

    Full Text Available Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains.

  2. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  3. Design and Evaluation of Two Blended Learning Approaches: Lessons Learned

    Science.gov (United States)

    Cheung, Wing Sum; Hew, Khe Foon

    2011-01-01

    In this paper, we share two blended learning approaches used at the National Institute of Education in Singapore. We have been using these two approaches in the last twelve years in many courses ranging from the diploma to graduate programs. For the first blended learning approach, we integrated one asynchronous communication tool with face to…

  4. Image-Based Learning Approach Applied to Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    J. C. Chimal-Eguía

    2012-06-01

    Full Text Available In this paper, a new learning approach based on time-series image information is presented. In order to implementthis new learning technique, a novel time-series input data representation is also defined. This input datarepresentation is based on information obtained by image axis division into boxes. The difference between this newinput data representation and the classical is that this technique is not time-dependent. This new information isimplemented in the new Image-Based Learning Approach (IBLA and by means of a probabilistic mechanism thislearning technique is applied to the interesting problem of time series forecasting. The experimental results indicatethat by using the methodology proposed in this article, it is possible to obtain better results than with the classicaltechniques such as artificial neuronal networks and support vector machines.

  5. A Learning Approach for Adaptive Image Segmentation

    OpenAIRE

    Martin, Vincent; Thonnat, Monique

    2007-01-01

    In this chapter, we have proposed a learning approach for three major issues of image segmentation: context adaptation, algorithm selection and parameter tuning according to the image content and the application need. This supervised learning approach relies on hand-labelled samples. The learning process is guided by the goal of the segmentation and therefore makes the approach reliable for a broad range of applications. The user effort is restrained compared to other supervised methods since...

  6. AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning

    OpenAIRE

    Bailey, T. M.; Pothos, E. M.

    2008-01-01

    Artificial Grammar Learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization based either on similarity or rules. Without computer assistance it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or non-grammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated soft...

  7. What Artificial Grammar Learning Reveals about the Neurobiology of Syntax

    Science.gov (United States)

    Petersson, Karl-Magnus; Folia, Vasiliki; Hagoort, Peter

    2012-01-01

    In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial…

  8. Project Management Approaches for Online Learning Design

    Science.gov (United States)

    Eby, Gulsun; Yuzer, T. Volkan

    2013-01-01

    Developments in online learning and its design are areas that continue to grow in order to enhance students' learning environments and experiences. However, in the implementation of new technologies, the importance of properly and fairly overseeing these courses is often undervalued. "Project Management Approaches for Online Learning Design"…

  9. Machine learning approaches in medical image analysis

    DEFF Research Database (Denmark)

    de Bruijne, Marleen

    2016-01-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols......, learning from weak labels, and interpretation and evaluation of results....

  10. 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

  11. Approaches to e-learning

    DEFF Research Database (Denmark)

    Hartvig, Susanne Akrawi; Petersson, Eva

    2013-01-01

    E-learning has made its entrance into educational institutions. Compared to traditional learning methods, e-learning has the benefit of enabling educational institutions to attract more students. E-learning not only opens up for an increased enrollment, it also gives students who would otherwise...... not be able to take the education to now get the possibility to do so. This paper introduces Axel Honneth’s theory on the need for recognition as a framework to understand the role and function of interaction in relation to e-learning. The paper argues that an increased focus on the dialectic relationship...

  12. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2015-01-01

    identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power......—they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...

  13. Investigating the Effects of Personality on Second Language Learning through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Meryem Karlık

    2016-06-01

    Full Text Available The aim of this research is to determine Second Language Acquisition and personality variable from affective factors analyzed by Artificial Neural Network in freshman class of both university students. This study presents an intelligent approach to the investigation of positive effects of personality on second language learning. For this purpose, watching TV, reading books, magazines, newspaper, listening to the radio, talking to a native English friend, and talking to people at school are investigated. The tool of our research is a survey (questionnaire to collect a data in order to quantify students ‘personality traits based on affective factors. The questionnaire consists of two parts. The first part consists of Yes/ No questions while the second part uses a 4 point Likert scale with 5 items that indicates what helped students personally to learn English. The participants were 160 students from two private universities in Bosnia and Herzegovina, International Burch University (90 students and International University of Sarajevo (70. The subjects’ major was English. The first part of the survey was analyzed using ANN, and the second part using statistical analysis. Both data analysis were processed by transferring answers to an Excel sheet. For each measure, mode, standard deviation, median were calculated to determine students’ personality factors. We used two different types of analysis in order to show that different kinds of analysis can be done.

  14. Timing matters: The impact of immediate and delayed feedback on artificial language learning

    Directory of Open Access Journals (Sweden)

    Bertram Opitz

    2011-02-01

    Full Text Available In the present experiment, we used event-related potentials (ERP to investigate the role of immediate and delayed feedback in an artificial grammar learning task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of one second. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP elicited by positive feedback only. The feedback-related negativity (FRN, however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for artificial grammar learning as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.

  15. A genetic-neural artificial intelligence approach to resins optimization

    International Nuclear Information System (INIS)

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  16. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    OpenAIRE

    Wenping Zou; Yunlong Zhu; Hanning Chen; Xin Sui

    2010-01-01

    Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorit...

  17. Pap, gruel, and panada: early approaches to artificial infant feeding.

    Science.gov (United States)

    Obladen, Michael

    2014-01-01

    This paper collects information on artificial infant feeding published before 1860, the year when commercial formula became available. We have extensive artifactual evidence of thousands of feeding vessels since the Bronze Age. Special museum collections can be found in London, Paris, Cologne, Fécamp, Toronto, New Mexico, and elsewhere. The literature on the use of animal milk for infant feeding begins with Soranus in the 2nd century CE. Literature evidence from the very first printed books in the 15th century proves that physicians, surgeons, midwives, and the laity were aware of the opportunities and risks of artificial infant feeding. Most 17th to 19th century books on infant care contained detailed recipes for one or several of the following infant foods: pap, a semisolid food made of flour or bread crumbs cooked in water with or without milk; gruel, a thin porridge resulting from boiling cereal in water or milk, and panada, a preparation of various cereals or bread cooked in broth. During the 18th century, the published opinion on artificial feeding evolved from health concerns to a moral ideology. This view ignored the social and economic pressures which forced many mothers to forego or shorten breast-feeding. Bottle-feeding has been common practice throughout history.

  18. Karst and artificial recharge: Theoretical and practical problems. A preliminary approach to artificial recharge assessment

    Science.gov (United States)

    Daher, Walid; Pistre, Séverin; Kneppers, Angeline; Bakalowicz, Michel; Najem, Wajdi

    2011-10-01

    SummaryManaged Aquifer Recharge (MAR) is an emerging sustainable technique that has already generated successful results and is expected to solve many water resource problems, especially in semi-arid and arid zones. It is of great interest for karst aquifers that currently supply 20-25% of the world's potable water, particularly in Mediterranean countries. However, the high heterogeneity in karst aquifers is too complex to be able to locate and describe them simply via field observations. Hence, as compared to projects in porous media, MAR is still marginal in karst aquifers. Accordingly, the present work presents a conceptual methodology for Aquifer Rechargeability Assessment in Karst - referred to as ARAK. The methodology was developed noting that artificial recharge in karst aquifers is considered an improbable challenge to solve since karst conduits may drain off recharge water without any significant storage, or recharge water may not be able to infiltrate. The aim of the ARAK method is to determine the ability of a given karst aquifer to be artificially recharged and managed, and the best sites for implementing artificial recharge from the surface. ARAK is based on multi-criteria indexation analysis modeled on karst vulnerability assessment methods. ARAK depends on four independent criteria, i.e. Epikarst, Rock, Infiltration and Karst. After dividing the karst domain into grids, these criteria are indexed using geological and topographic maps refined by field observations. ARAK applies a linear formula that computes the intrinsic rechargeability index based on the indexed map for every criterion, coupled with its attributed weighting rate. This index indicates the aptitude for recharging a given karst aquifer, as determined by studying its probability first on a regional scale for the whole karst aquifer, and then by characterizing the most favorable sites. Subsequently, for the selected sites, a technical and economic feasibility factor is applied, weighted

  19. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  20. Lab classes in chemistry learning an artificial intelligence view

    OpenAIRE

    Figueiredo, Margarida; Esteves, M. Lurdes; Neves, José; Vicente, Henrique (Orientador)

    2014-01-01

    The teaching methodology used in lab classes in Chemistry Learning was studied for a cohort of 702 students in the 10th grade of Portuguese Secondary Schools. The k-Means Clustering Method, with k values ranging between 2 (two) and 4 (four), was used in order to segment the data. Decision Trees were used for the development of explanatory models of the segmentation. The results obtained showed that the majority of the answerers considered that experimentation is central on Chemistry learning....

  1. Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach

    International Nuclear Information System (INIS)

    This study presents an application of the artificial neural network (ANN) model using the back propagation (BP) learning algorithm to predict the performance (suction line outlet temperature and mass flow rate) of a non-adiabatic capillary tube suction line heat exchanger, basically used as a throttling device in small household refrigeration systems. Comparative studies were made by using an ANN model, experimental results and correlations to predict the performance. These studies showed that the proposed approach could successfully be used for performance prediction for the exchanger

  2. ICON: An artificial intelligence approach to radiologic differential diagnosis

    International Nuclear Information System (INIS)

    ICON is a computer system, developed using artificial intelligence techniques, that is designed to help radiologists manage the large body of knowledge needed to perform differential diagnosis in radiology. The system's domain is lung disease in patients with lymphoproliferative disorders. The radiologist proposes a diagnostic hypothesis which he or she thinks explains the known clinical and chest radiographic findings. ICON responds with an English-language prose critique that discusses how and why the proposed diagnosis is or is not supported by the clinical literature and suggests further findings or clinical information that might make the diagnosis more secure

  3. Learning morphological phenomena of modern Greek an exploratory approach

    Directory of Open Access Journals (Sweden)

    Y. Kotsanis

    1996-12-01

    Full Text Available Educational technology is influenced by and closely related to the fields of generative epistemology, Artificial Intelligence, and the learning sciences. Relevant research literature refers to the term constructionism (Papert, 1993 and exploratory learning (diSessa et al, 1995. Constructionism and exploratory learning are a synthesis of the constructivist theory of Piaget and the opportunities offered by technology to education on thinking concretely, on learning while constructing intelligible entities, and on interacting with multimedia objects, rather than the direct acquisition of knowledge and facts. These views are based on the approach that learners can take substantial control of their own learning in an appropriately designed physical and cultural environment (Harel, 1991. In parallel, most of the studies of the Vygotskian framework focus on the role of language in the learning procedure, considering conceptual thought to be impossible outside an articulated verbal thinking. Moreover, the specific use of words is considered to be the most relevant cause for childhood and adolescent differentiation (Vygotsky, 1962.

  4. Application of Artificial Neural Network Approach for Estimating Reference Evapotranspiration

    Directory of Open Access Journals (Sweden)

    Khyati N. Vyas

    2016-08-01

    Full Text Available The process of evapotranspiration (ET is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0 among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN for estimating ET0 with limited climatic data set.

  5. Predicting oil price movements: A dynamic Artificial Neural Network approach

    International Nuclear Information System (INIS)

    Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. - Highlights: • Nonlinear Auto Regressive model with eXogenous (NARX) inputs is developed for predicting oil prices. • The results of NARX model in oil price forecasting is more accurate than those of time series and Artificial Neural Network. • The NARX model predicts the price shocks in the oil market. • The NARX model is dynamic and accounts for the factor of time

  6. A Learning Approach to Auctions

    OpenAIRE

    Hon-Snir, Shlomit; Monderer, Dov; Sela, Aner

    1996-01-01

    We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then for sufficiently large time, the players' bids are in equilibrium in the one-shot auction in which the types are commonly known.

  7. Simulation Techniques and Prosthetic Approach Towards Biologically Efficient Artificial Sense Organs- An Overview

    CERN Document Server

    Neogi, Biswarup; Mukherjee, Soumyajit; Das, Achintya; Tibarewala, D N

    2011-01-01

    An overview of the applications of control theory to prosthetic sense organs including the senses of vision, taste and odor is being presented in this paper. Simulation aspect nowadays has been the centre of research in the field of prosthesis. There have been various successful applications of prosthetic organs, in case of natural biological organs dis-functioning patients. Simulation aspects and control modeling are indispensible for knowing system performance, and to generate an original approach of artificial organs. This overview focuses mainly on control techniques, by far a theoretical overview and fusion of artificial sense organs trying to mimic the efficacies of biologically active sensory organs. Keywords: virtual reality, prosthetic vision, artificial

  8. Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.

    Science.gov (United States)

    Wilson, Benjamin; Smith, Kenny; Petkov, Christopher I

    2015-03-01

    Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans.

  9. Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

    OpenAIRE

    Mehrkesh, Amirhossein; Ahmadi, Maryam

    2014-01-01

    Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colo...

  10. Template Approach for Adaptive Learning Strategies

    NARCIS (Netherlands)

    Abbing, Jana; Koidl, Kevin

    2006-01-01

    Please, cite this publication as: Abbing, J. & Koidl, K. (2006). Template Approach for Adaptive Learning Strategies. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  11. Study strategies and approaches to learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter

    In this study students’ study strategies have been compared to their approaches to learning. The time students spend on different study activities has been investigated at the Technical University of Denmark, and as a pilot project a few students also filled in a reduced version of Bigg's Study...... Process Questionnaire to identify their approach to learning. It was hypothesised that the students’ learning approach would depend more on the quality of the study work than on the quantity; that an active and reflective study strategy was required to obtain deep conceptual understanding. The result....... There was however a much stronger linear correlation (significant at the 0.01 level) between the deep-surface ratio and the total study load. The same result was observed when measuring other students’ study strategy and learning approach for a single course. The empirical basis is still too limited to draw...

  12. Cultivating collaborative improvement: an action learning approach

    NARCIS (Netherlands)

    Middel, Rick; McNichols, Timothy

    2006-01-01

    The process of implementing collaborative initiatives across disparate members of supply networks is fraught with difficulties. One approach designed to tackle the difficulties of organisational change and interorganisational improvement in practice is 'action learning'. This paper examines the expe

  13. The Point Approach and the Phrase Approach to Vocabulary Learning

    Institute of Scientific and Technical Information of China (English)

    刘梦媛

    2013-01-01

      As is known to all, vocabulary acquisition plays an essential role in English learning. However, it was supposed very dif⁃ficult to many Chinese learners. For the reason that so many kinds of approaches exists in the real life, English learners are always do not know which one is suitable and more effective. To solve this problem, the paper will analyze two approaches (point approach and phrase approach) for you.

  14. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wenping Zou

    2010-01-01

    Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

  15. Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2013-07-01

    Full Text Available Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results.

  16. Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach

    Energy Technology Data Exchange (ETDEWEB)

    Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    2000-01-12

    The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained.

  17. Artificial photosynthesis: biomimetic approaches to solar energy conversion and storage.

    Science.gov (United States)

    Kalyanasundaram, K; Graetzel, M

    2010-06-01

    Using sun as the energy source, natural photosynthesis carries out a number of useful reactions such as oxidation of water to molecular oxygen and fixation of CO(2) in the form of sugars. These are achieved through a series of light-induced multi-electron-transfer reactions involving chlorophylls in a special arrangement and several other species including specific enzymes. Artificial photosynthesis attempts to reconstruct these key processes in simpler model systems such that solar energy and abundant natural resources can be used to generate high energy fuels and restrict the amount of CO(2) in the atmosphere. Details of few model catalytic systems that lead to clean oxidation of water to H(2) and O(2), photoelectrochemical solar cells for the direct conversion of sunlight to electricity, solar cells for total decomposition of water and catalytic systems for fixation of CO(2) to fuels such as methanol and methane are reviewed here.

  18. A Machine Learning Approach to Automated Negotiation

    Institute of Scientific and Technical Information of China (English)

    Zhang Huaxiang(张化祥); Zhang Liang; Huang Shangteng; Ma Fanyuan

    2004-01-01

    Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents' average Q values in each negotiation stage. The delayed reward is used to generate agents' offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.

  19. Machine learning a theoretical approach

    CERN Document Server

    Natarajan, Balas K

    2014-01-01

    This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation

  20. Pedagogical quality in e-learning - Designing e-learning from a learning theoretical approach

    OpenAIRE

    Dalsgaard, Christian

    2005-01-01

    The article is concerned with design and use of e-learning technology to develop education qualitatively. The purpose is to develop a framework for a pedagogical evaluation of e-learning technology. The approach is that evaluation and design must be grounded in a learning theoretical approach, and it is argued that it is necessary to make a reflection of technology in relation to activities, learning principles, and a learning theory in order to qualitatively develop education. The article pr...

  1. An approach to elemental task learning

    Energy Technology Data Exchange (ETDEWEB)

    Belmans, P

    1990-01-01

    In this article we deal with the automated learning of tasks by a robotic system through observation of a human operator. Particularly, we explain what is meant by a learning ability in autonomous robots and in teleoperation systems, where several operators and several machines may work in cooperation to perform tasks. We discuss different approaches to learning in these systems and outline the features of the models they are based upon. This leads us to choose an analytical model suited for tasks analysis. We then present the software architecture for our proposed approach and show the first results obtained on sample tests. 5 refs., 9 figs.

  2. Cultural differences in learning approaches

    NARCIS (Netherlands)

    Tempelaar, D.T.; Rienties, B.C.; Giesbers, S.J.H.; Schim van der Loeff, S.; Van den Bossche, P.; Gijselaers, W.H.; Milter, R.G.

    2013-01-01

    Cultural differences in learning-related dispositions are investigated amongst 7,300 first year students from 81 different nationalities, using the framework of Hofstede (Culture’s consequences: international differences in work-related values. Sage, Beverly Hills, 1980). Comparing levels and interc

  3. A Cultural Approach to Learning

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard

    1998-01-01

    The this article learning is discussed in relation to different understanding of culture. In particular the dialectics of 'Enlightenment' inthe Western culture are reflected , as well aslow- and high-context communication and learningin different types of culture. Finaaly the Weberian methodology...

  4. Social Learning in Bumblebees (Bombus impatiens): Worker Bumblebees Learn to Manipulate and Forage at Artificial Flowers by Observation and Communication within the Colony

    OpenAIRE

    Hamida B. Mirwan; Peter G. Kevan

    2013-01-01

    Social learning occurs when one individual learns from another, mainly conspecific, often by observation, imitation, or communication. Using artificial flowers, we studied social learning by allowing test bumblebees to (a) see dead bumblebees arranged in foraging positions or (b) watch live bumblebees actually foraging or (c) communicate with nestmates within their colony without having seen foraging. Artificial flowers made from 1.5 mL microcentrifuge tubes with closed caps were inserted thr...

  5. Applying artificial intelligence to clinical guidelines: the GLARE approach.

    Science.gov (United States)

    Terenziani, Paolo; Montani, Stefania; Bottrighi, Alessio; Molino, Gianpaolo; Torchio, Mauro

    2008-01-01

    We present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines (GL). GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed. Second, a user-friendly acquisition tool, which provides expert physicians with various forms of help, has been implemented. Third, a tool for executing GL on a specific patient has been made available. At all the levels above, advanced AI techniques have been exploited, in order to enhance flexibility and user-friendliness and to provide decision support. Specifically, this chapter focuses on the methods we have developed in order to cope with (i) automatic resource-based adaptation of GL, (ii) representation and reasoning about temporal constraints in GL, (iii) decision making support, and (iv) model-based verification. We stress that, although we have devised such techniques within the GLARE project, they are mostly system-independent, so that they might be applied to other guideline management systems.

  6. 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

  7. Concept Learning for Achieving Personalized Ontologies: An Active Learning Approach

    Science.gov (United States)

    Şensoy, Murat; Yolum, Pinar

    In many multiagent approaches, it is usual to assume the existence of a common ontology among agents. However, in dynamic systems, the existence of such an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are different from each other. Therefore, to achieve personalized ontologies, agents must have a means to understand the concepts used by others. Consequently, this paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enables the learner to elicit most informative examples interactively from the teacher. Hence, the learner participates to the learning process actively. We empirically compare the proposed approach with the previous concept learning approaches. Our experiments show that using the proposed approach, agents can learn new concepts successfully and with fewer examples.

  8. THE FACT APPROACH TO COLLEGE ENGLISH LEARNING

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Introduction The proliferation of approaches and methods is a prominent characteristic of contemporary ESL or EFL teaching. From the Grammar Translation Method, the Direct Method, Situational Language Teaching, the Audiolingual Method to Communicative Language Teaching, Total Physical Response, the Silent Way, Community Language Learning, the Natural Approach and Suggestopedia, the discussion has lasted for more than a century. However, the wide variety of method or approach options confuses rather than comforts, because they seem to hold very different views of what language is and how a language is learned (Richards & Rodgers, 1991) . This confusing situation is mainly caused by the myth of the hu-

  9. A Learning Object Approach To Evidence based learning

    OpenAIRE

    Zabin Visram; Bruce Elson; Patricia Reynolds

    2005-01-01

    This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has mea...

  10. Artificial Intelligence in Education: An Exploration.

    Science.gov (United States)

    Cumming, Geoff

    1998-01-01

    Gives a brief outline of the development of Artificial Intelligence in Education (AIED) which includes psychology, education, cognitive science, computer science, and artificial intelligence. Highlights include learning environments; learner modeling; a situated approach to learning; and current examples of AIED research. (LRW)

  11. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  12. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  13. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  14. Artificial Neural Network Approach for Predicting Performance of Multi-Agent Systems Using SPE Approach

    Directory of Open Access Journals (Sweden)

    S. Ajitha

    2013-07-01

    Full Text Available Performance is a persistent quality of any software systems. Software perfor-mance engineering (SPE encompasses efforts to describe and improve per-formance of systems at the early stages of development of the system. Multi-Agent Systems (MAS are composed of autonomous entities called agents which cooperates together to solve complex distributed problems. Whatever complex the system, the quality of the system is an important parameter to be addressed. In this paper we are proposing an algorithm for predicting the per-formance of softwfrfare systems using Artificial Neural Network (ANN ap-proach. The algorithm is a new attempt in performance engineering of MAS. We have used ANN models for size estimation of the software (representative workload which is an important parameter for assessing the performance in early stages of software development. Another significant contribution is as-sessment of performance by considering the data gathered during feasibility study. The ANN models are trained and validated for different data sets. The algorithm is validated for static properties of the RETSINA architecture. A case study on MAS is considered and the results are obtained using the vali-dated ANN model.

  15. Connections from Kafka: exposure to meaning threats improves implicit learning of an artificial grammar.

    Science.gov (United States)

    Proulx, Travis; Heine, Steven J

    2009-09-01

    In the current studies, we tested the prediction that learning of novel patterns of association would be enhanced in response to unrelated meaning threats. This prediction derives from the meaning-maintenance model, which hypothesizes that meaning-maintenance efforts may recruit patterns of association unrelated to the original meaning threat. Compared with participants in control conditions, participants exposed to either of two unrelated meaning threats (i.e., reading an absurd short story by Franz Kafka or arguing against one's own self-unity) demonstrated both a heightened motivation to perceive the presence of patterns within letter strings and enhanced learning of a novel pattern actually embedded within letter strings (artificial-grammar learning task). These results suggest that the cognitive mechanisms responsible for implicitly learning patterns are enhanced by the presence of a meaning threat.

  16. Modelling fed-batch fermentation processes : an approach based on artificial neural networks

    OpenAIRE

    Valente, Eduardo; Rocha, I; Rocha, Miguel

    2009-01-01

    Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7 Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work,...

  17. The Activity Theory Approach to Learning

    Directory of Open Access Journals (Sweden)

    Ritva Engeström

    2014-12-01

    Full Text Available In this paper the author offers a practical view of the theory-grounded research on education action. She draws on studies carried out at the Center for Research on Activity, Development and Learning (CRADLE at the University of Helsinki in Finland. In its work, the Center draws on cultural-historical activity theory (CHAT and is well-known for the theory of Expansive Learning and its more practical application called Developmental Work Research (DWR. These approaches are widely used to understand professional learning and have served as a theoreticaland methodological foundation for studies examining change and professional development in various human activities.

  18. ASSESSMENT APPROACHES IN VIRTUAL LEARNING

    Directory of Open Access Journals (Sweden)

    Azam RASTGOO

    2010-01-01

    Full Text Available Today, the traditional assessment methods are not enough for measuring students’ ability. Internet and its technologies have a strong impact to change it and there are some new ways to measure students’ ability and knowledge. This article identifies some assessment methods and tools in online education and describes findings that show the importance of online assessment and online technologies. It also describes some advantages and disadvantages of new methods of assessment. Additionally this article review some valuable effects of using new methods such as e-portfolios, online self and peer-assessment in providing in time and good feedback for student, increasing students’ participation, improving students learning achievement and increasing their abilities in this area.

  19. Bilingual Approaches to Language Learning

    Institute of Scientific and Technical Information of China (English)

    MaryMcgroarty; NorthernArizonaUniversity

    2003-01-01

    “Billingual Approaches to Language Learning” describes the various bilingual models found at different levels(elementary,secondary,post-secondary,and adult education),identifying key instructional features and emphasizing the drive for quality instruction.The paper makes reference to consideration of the political contexts as well as pedagogical factors affecting the choices and outcomes related to bilingual instruction.

  20. An artificial intelligence approach in designing new materials

    OpenAIRE

    W. Sitek; J. Trzaska; L.A. Dobrzański

    2006-01-01

    Purpose: The paper presents the computer aided method of chemical composition designing the metallic materials with a required property.Design/methodology/approach: The purpose has been achieved in two stages. In the first stage a neural network model for calculating the Jominy curve on the basis of the chemical composition has been worked out. This model made possible to prepare, in the second stage, a representative set of data and to work out the neural classifier that would aid the select...

  1. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    OpenAIRE

    Terry Howell; Indrajeet Chaubey; Prasanna Gowda; K. P. Sudheer

    2010-01-01

    Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remo...

  2. Learning and long-term retention of large-scale artificial languages.

    Directory of Open Access Journals (Sweden)

    Michael C Frank

    Full Text Available Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of "statistical learning" can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.

  3. The influence of consistency, frequency, and semantics on learning to read: an artificial orthography paradigm.

    Science.gov (United States)

    Taylor, J S H; Plunkett, Kim; Nation, Kate

    2011-01-01

    Two experiments explored learning, generalization, and the influence of semantics on orthographic processing in an artificial language. In Experiment 1, 16 adults learned to read 36 novel words written in novel characters. Posttraining, participants discriminated trained from untrained items and generalized to novel items, demonstrating extraction of individual character sounds. Frequency and consistency effects in learning and generalization showed that participants were sensitive to the statistics of their learning environment. In Experiment 2, 32 participants were preexposed to the sounds of all items (lexical phonology) and to novel definitions for half of these items (semantics). Preexposure to either lexical phonology or semantics boosted the early stages of orthographic learning relative to Experiment 1. By the end of training, facilitation was restricted to the semantic condition and to items containing low-frequency inconsistent vowels. Preexposure reduced generalization, suggesting that enhanced item-specific learning was achieved at the expense of character-sound abstraction. The authors' novel paradigm provides a new tool to explore orthographic learning. Although the present findings support the idea that semantic knowledge supports word reading processes, they also suggest that item-specific phonological knowledge is important in the early stages of learning to read.

  4. Measuring approaches to learning in a problem based learning context

    Directory of Open Access Journals (Sweden)

    Diana H. Dolmans

    2010-07-01

    Full Text Available Objectives: Students in Problem-Based Learning (PBL are assumed to adopt a deep learning approach and not a surface approach. This study investigated: 1 the reliability and validity of version of the Revised Study Process Questionnaire adapted to the PBL context (PBL-R-SPQ and 2 the extent to which PBL students use deep or surface approaches, and whether this differs between first and second year students. Methods: The items of the R-SPQ were reformulated to better fit with a PBL environment, resulting in the PBL-R-SPQ. In total 262 students from MaastrichtMedicalSchool responded to the PBL-R-SPQ. Results: A Confirmatory Factor Analysis (CFA demon-strated that a 9-item Deep Approach scale and a 9-item Surface Approach scale fitted the observed set of data well. Cronbach alphas for the Deep and Surface scales were 0.76 and 0.74, respectively. First year students reported signifi cantly higher Deep Approach scores (M = 3.60, SD = .48 than second year students (M = 3.40, SD = .48 (p = .001, d = .42. Conversely, second year students reported signifi-cantly higher Surface Approach scores, (M = 2.45, SD = .48 than first year students (M = 2.26, SD = .52 (p = .003, d = .38. Conclusions: The 18-item PBL-R-SPQ provides a valid and reliable tool to measure students' learning approach in PBL. In addition, PBL students tended to adopt a deep approach rather than a surface approach, which is in line with the assumptions behind PBL, although the second year students have a somewhat less deep approach than the first year students.

  5. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    Science.gov (United States)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  6. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  7. 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

  8. Artificial intelligence and tutoring systems computational and cognitive approaches to the communication of knowledge

    CERN Document Server

    Wenger, Etienne

    2014-01-01

    Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge focuses on the cognitive approaches, methodologies, principles, and concepts involved in the communication of knowledge. The publication first elaborates on knowledge communication systems, basic issues, and tutorial dialogues. Concerns cover natural reasoning and tutorial dialogues, shift from local strategies to multiple mental models, domain knowledge, pedagogical knowledge, implicit versus explicit encoding of knowledge, knowledge communication, and practical and theoretic

  9. Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks

    OpenAIRE

    Ņikitenko, A; Liekna, A; Ekmanis, M.; Kuļikovskis, G; Andersone, I

    2013-01-01

    we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer – based localization into one approach.

  10. Artificial intelligence approach in analysis of DNA sequences.

    Science.gov (United States)

    Brézillon, P J; Zaraté, P; Saci, F

    1993-01-01

    We present an approach for designing a knowledge-based system, called Sequence Acquisition In Context (SAIC), that will be able to cooperate with a biologist in the analysis of DNA sequences. The main task of the system is the acquisition of the expert knowledge that the biologist uses for solving ambiguities from gel autoradiograms, with the aim of re-using it later for solving similar ambiguities. The various types of expert knowledge constitute what we call the contextual knowledge of the sequence analysis. Contextual knowledge deals with the unavoidable problems that are common in the study of the living material (eg noise on data, difficulties of observations). Indeed, the analysis of DNA sequences from autoradiograms belongs to an emerging and promising area of investigation, namely reasoning with images. The SAIC project is developed in a theoretical framework that is shared with other applications. Not all tasks have the same importance in each application. We use this observation for designing an intelligent assistant system with three applications. In the SAIC project, we focus on knowledge acquisition, human-computer interaction and explanation. The project will benefit research in the two other applications. We also discuss our SAIC project in the context of large international projects that aim to re-use and share knowledge in a repository.

  11. A theoretical approach to artificial intelligence systems in medicine.

    Science.gov (United States)

    Spyropoulos, B; Papagounos, G

    1995-10-01

    The various theoretical models of disease, the nosology which is accepted by the medical community and the prevalent logic of diagnosis determine both the medical approach as well as the development of the relevant technology including the structure and function of the A.I. systems involved. A.I. systems in medicine, in addition to the specific parameters which enable them to reach a diagnostic and/or therapeutic proposal, entail implicitly theoretical assumptions and socio-cultural attitudes which prejudice the orientation and the final outcome of the procedure. The various models -causal, probabilistic, case-based etc. -are critically examined and their ethical and methodological limitations are brought to light. The lack of a self-consistent theoretical framework in medicine, the multi-faceted character of the human organism as well as the non-explicit nature of the theoretical assumptions involved in A.I. systems restrict them to the role of decision supporting "instruments" rather than regarding them as decision making "devices". This supporting role and, especially, the important function which A.I. systems should have in the structure, the methods and the content of medical education underscore the need of further research in the theoretical aspects and the actual development of such systems.

  12. A Learning Object Approach To Evidence based learning

    Directory of Open Access Journals (Sweden)

    Zabin Visram

    2005-06-01

    Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

  13. Approaches to learning of Irish students studying accounting

    OpenAIRE

    Byrne, Marann; Flood, Barbara; Willis, Pauline

    1999-01-01

    Several reports on accounting education have identified the development of students' learning to learn as the primary objective of accounting education. Higher education research identifies the approach to learning as a significant factor in the overall student learning experience. If accounting educators are to find ways to improve the educational experience of their students, they must understand how students learn and the effects of the learning context on learning approaches. This ...

  14. A Sociocultural Approach to Recognition and Learning

    Directory of Open Access Journals (Sweden)

    Peter Musaeus

    2006-04-01

    Full Text Available This is a case study of goldsmith craft apprenticeship learning and recognition. The study includes 13 participants in a goldsmith's workshop. The theoretical approach to recognition and learning is inspired by sociocultural theory. In this article recognition is defined with reference to Hegel’s understanding of the concept as a transformed struggle of granting acknowledgement to another person plus receiving acknowledgement as a person. It is argued that the notion of recognition can enhance sociocultural notions of learning. In analysing the case study of apprenticeship learning, the article suggests that recognition is expressed in the act of participants staking their lives to prove their autonomy, in work activity in terms of the role of artefacts and in the form of abstract and concrete recognition. Finally recognition is discussed in relation to learning and development. The study concludes that recognition is an important category not only to explain apprenticeship learning but also to give a sociocultural explanation of learning in general.

  15. Young Children's Approaches to Learning: A Sociocultural Perspective

    Science.gov (United States)

    Chen, Jie-Qi; Masur, Ann; McNamee, Gillian

    2011-01-01

    Recognising school readiness as a national priority, the National Education Goals Panel identified the development of young children's approaches to learning as essential for achieving readiness. Approaches to learning include inclinations, attitudes, and learning styles. Despite wide agreement that learning approaches are critical for school…

  16. A Mixed Learning Approach in Mechatronics Education

    Science.gov (United States)

    Yilmaz, O.; Tuncalp, K.

    2011-01-01

    This study aims to investigate the effect of a Web-based mixed learning approach model on mechatronics education. The model combines different perception methods such as reading, listening, and speaking and practice methods developed in accordance with the vocational background of students enrolled in the course Electromechanical Systems in…

  17. Transformative Learning Approaches for Public Relations Pedagogy

    Science.gov (United States)

    Motion, Judy; Burgess, Lois

    2014-01-01

    Public relations educators are frequently challenged by students' flawed perceptions of public relations. Two contrasting case studies are presented in this paper to illustrate how socially-oriented paradigms may be applied to a real-client project to deliver a transformative learning experience. A discourse-analytic approach is applied…

  18. PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Nick Z. Zacharis

    2016-09-01

    Full Text Available Along with the spreading of online education, the importance of active support of students involved in online learning processes has grown. The application of artificial intelligence in education allows instructors to analyze data extracted from university servers, identify patterns of student behavior and develop interventions for struggling students. This study used student data stored in a Moodle server and predicted student success in course, based on four learning activities - communication via emails, collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict student performance on a blended learning course environment. The model predicted the performance of students with correct classification rate, CCR, of 98.3%.

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

    Science.gov (United States)

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

    2015-11-01

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

  20. Design of Learning Objects for Concept Learning: Effects of Multimedia Learning Principles and an Instructional Approach

    Science.gov (United States)

    Chiu, Thomas K. F.; Churchill, Daniel

    2016-01-01

    Literature suggests using multimedia learning principles in the design of instructional material. However, these principles may not be sufficient for the design of learning objects for concept learning in mathematics. This paper reports on an experimental study that investigated the effects of an instructional approach, which includes two teaching…

  1. Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm.

    Science.gov (United States)

    Pavlidou, Elpis V; Williams, Joanne M

    2014-07-01

    We examined implicit learning in school-aged children with and without developmental dyslexia based on the proposal that implicit learning plays a significant role in mastering fluent reading. We ran two experiments with 16 typically developing children (9 to 11-years-old) and 16 age-matched children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. In Experiment 1 (non-transfer task), children were trained on stimuli that followed patterns (rules) unknown to them. Subsequently, they were asked to decide from a novel set which stimuli follow the same rules (grammaticality judgments). In Experiment 2 (transfer task), training and testing stimuli differed in their superficial characteristics but followed the same rules. Again, children were asked to make grammaticality judgments. Our findings expand upon previous research by showing that children with developmental dyslexia show difficulties in implicit learning that are most likely specific to higher-order rule-like learning. These findings are discussed in relation to current theories of developmental dyslexia and of implicit learning.

  2. Games and machine learning: a powerful combination in an artificial intelligence course

    Science.gov (United States)

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-03-01

    Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

  3. Semi-Supervised Learning Techniques in AO Applications: A Novel Approach To Drift Counteraction

    Science.gov (United States)

    De Vito, S.; Fattoruso, G.; Pardo, M.; Tortorella, F.; Di Francia, G.

    2011-11-01

    In this work we proposed and tested the use of SSL techniques in the AO domain. The SSL characteristics have been exploited to reduce the need for costly supervised samples and the effects of time dependant drift of state-of-the-art statistical learning approaches. For this purpose, an on-field recorded one year long atmospheric pollution dataset has been used. The semi-supervised approach benefitted from the use of updated unlabeled samples, adapting its knowledge to the slowly changing drift effects. We expect that semi-supervised learning can provide significant advantages to the performance of sensor fusion subsystems in artificial olfaction exhibiting an interesting drift counteraction effect.

  4. A Bayesian Concept Learning Approach to Crowdsourcing

    DEFF Research Database (Denmark)

    Viappiani, Paolo Renato; Zilles, Sandra; Hamilton, Howard J.;

    2011-01-01

    We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...... techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing...... that our Bayesian strategies are effective even in large concept spaces with many uninformative experts....

  5. Learning Approaches of Successful Students and Factors Affecting Their Learning Approaches

    OpenAIRE

    İlhan Beyaztaş, Dilek; Erzincan Üniversitesi, Eğitim Fakültesi, İlköğretim Bölümü; Senemoğlu, Nuray; Hacettepe Üniversitesi, Eğitim Fakültesi, Eğitim Bilimleri Bölümü

    2015-01-01

    The purpose of this descriptive study is to identify learning approaches (deep, surface, or strategic) among successful undergraduate students and the factors that affect and shape their learning approaches. The study sample comprised 90 freshman students who were ranked in the top one percent portion of the 2013 University Placement Exam (UPE) in Turkey. Students were variously attending faculties of education, law or medicine and were grouped in subject areas of Literacy-Social (LS), Litera...

  6. An Artificial Intelligence Approach to the Symbolic Factorization of Multivariable Polynomials. Technical Report No. CS74019-R.

    Science.gov (United States)

    Claybrook, Billy G.

    A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and…

  7. Effect of signal noise on the learning capability of an artificial neural network

    International Nuclear Information System (INIS)

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  8. Learning Matlab a problem solving approach

    CERN Document Server

    Gander, Walter

    2015-01-01

    This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014.  Teaching and learning a substantial programming language aren’t always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

  9. Information theory and learning a physical approach

    CERN Document Server

    Nemenman, I M

    2000-01-01

    We try to establish a unified information theoretic approach to learning and to explore some of its applications. First, we define {\\em predictive information} as the mutual information between the past and the future of a time series, discuss its behavior as a function of the length of the series, and explain how other quantities of interest studied previously in learning theory - as well as in dynamical systems and statistical mechanics - emerge from this universally definable concept. We then prove that predictive information provides the {\\em unique measure for the complexity} of dynamics underlying the time series and show that there are classes of models characterized by {\\em power-law growth of the predictive information} that are qualitatively more complex than any of the systems that have been investigated before. Further, we investigate numerically the learning of a nonparametric probability density, which is an example of a problem with power-law complexity, and show that the proper Bayesian formul...

  10. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-04-01

    Full Text Available This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1 the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2 the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.

  11. Teaching European Studies: A Blended Learning Approach

    Directory of Open Access Journals (Sweden)

    Alina Christova

    2011-12-01

    Full Text Available This paper will be looking into the teaching method developed by the Institute for European Studies in Brussels, combining an e-learning tool- the E-modules- with face-to-face training sessions and webinars. The main aim is to analyse the three different components of this “blended learning” pedagogical approach, as well as the way they complement each other and to address a few of the challenges that have emerged from the experience of working with them so far. The E-modules are an e-learning platform that has been designed with the purpose of offering a structured and interactive way of learning how the European Union functions. The face-to-face training component currently takes the form of three days in-house seminars, covering in an intensive manner the most important areas of the curriculum. The lectures are held by a mix of academics and practitioners, hereby ensuring a balanced approach, in which theory and practice come together to facilitate the learning experience. The third element of the “blended learning” method is placed in-between online and face-to-face learning: interactive seminars and debates are held online, giving the participants the chance to deepen their knowledge in certain fields of interest and to discuss the content of the course with specialists and among themselves. The mixture of delivery and interaction methods was chosen in order to accommodate a large variety of target groups, ranging from students to professionals working with EU-related issues, with different backgrounds and geographical origins. One of the main challenges is to use each medium for the functionalities it is best designed for and to ensure that the various pieces of the pedagogical puzzle fit together perfectly, while allowing the learners the flexibility that had initially directed them towards “blended learning” instead of a classical classroom approach.

  12. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

    OpenAIRE

    Yudong Zhang; Lenan Wu

    2011-01-01

    This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The ex...

  13. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    OpenAIRE

    Hong-Hai Tran; Nhat-Duc Hoang

    2014-01-01

    Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is u...

  14. Economic Gardening through Entrepreneurship Education: A Service-Learning Approach

    Science.gov (United States)

    Desplaces, David E.; Wergeles, Fred; McGuigan, Patrick

    2009-01-01

    This article outlines the implementation of a service-learning approach in an entrepreneurship programme using an "economic gardening" strategy. Economic Gardening through Service-Learning (EGS-L) is an approach to economic development that helps local businesses and students grow through a facilitated learning process. Learning is made possible…

  15. Understanding Fatty Acid Metabolism through an Active Learning Approach

    Science.gov (United States)

    Fardilha, M.; Schrader, M.; da Cruz e Silva, O. A. B.; da Cruz e Silva, E. F.

    2010-01-01

    A multi-method active learning approach (MALA) was implemented in the Medical Biochemistry teaching unit of the Biomedical Sciences degree at the University of Aveiro, using problem-based learning as the main learning approach. In this type of learning strategy, students are involved beyond the mere exercise of being taught by listening. Less…

  16. Learning Strategies Under the Process Writing Approach

    Institute of Scientific and Technical Information of China (English)

    肖青芝

    2008-01-01

    Learning strategies play an important role in second language writing.It is even believed by some linguists that experienced writers and novice writers differ mainly in their writing strategies instead of language proficiency.Within the theoretical framework of the process writing approach,this article introduces the writing strategies with all attempt to increase students'awareness of strategy use and improve their English writing.

  17. Artificial trachea reconstruction with two-stage approach using memory-alloy mesh

    Institute of Scientific and Technical Information of China (English)

    赵凤瑞; 张银合; 刘世民; 余建军

    2003-01-01

    @@ Artificial tracheal prosthesis is now a challenge to the entire surgical field all over the world. Previously, all kinds of prosthesis were "inner stent" that could not be integrated with native trachea. Since there is an interface between smooth surface of the prosthesis and living tissues, and the inner side of the prosthesis is not covered with living membrane, infections always happen around the prosthesis. We then developed a new technique, which combined memory-alloy mesh with traditional operative procedures. Memory-alloy mesh is extensible, flexible and can maintain the shape of a tube. It has very good compatibility with tissues and no antigenicity. Thus, it is the most desirable material nowadays that can be found to make the frame of an artificial trachea by using a two-stage approach. That is imbedding a pre-shaped memory-alloy mesh under skin or endothelium (pleura or peritoneum), and then making the "sandwich" pedicle skin and muscle flap to a pedicled artificial trachea anastomosed with native trachea. After a two-year experimental study, a patient received the artificial trachea replacement on April 18, 2002, with a good result.

  18. Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

    CERN Document Server

    Ghosh, Arka

    2011-01-01

    Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.

  19. IDENTIFICATION OF ERYTHEMATO-SQUAMOUS SKIN DISEASES USING EXTREME LEARNING MACHINE AND ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Sunday Olusanya Olatunji

    2013-10-01

    Full Text Available In this work, a new identification model, based on extreme learning machine (ELM, to better identify Erythemato – Squamous skin diseases have been proposed and implemented and the results compared to that of the classical artificial neural network (ANN. ELMs provide solutions to single- and multi- hidden layer feed-forward neural networks. ELMs can achieve high learning speed, good generalization performance, and ease of implementation. Experimental results indicated that ELM outperformed the classical ANN in all fronts both for the training and testing cases. The effect of varying size of training and testing set on the performance of classifiers were also investigated in this study. The proposed classifier demonstrated to be a viable tool in this germane field of medical diagnosis as indicated by its high accuracy and consistency of result.

  20. ChemApproach: Validation of a Questionnaire to Assess the Learning Approaches of Chemistry Students

    Science.gov (United States)

    Lastusaari, Mika; Laakkonen, Eero; Murtonen, Mari

    2016-01-01

    The theory of learning approaches has proven to be one of the most powerful theories explaining university students' learning. However, learning approaches are sensitive to the situation and the content of learning. Chemistry has its own specific features that should be considered when exploring chemistry students' learning habits, specifically…

  1. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    Science.gov (United States)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  2. ABC Tester - Artificial Bee Colony Based Software Test Suite Optimization Approach

    Directory of Open Access Journals (Sweden)

    D. Jeya Mala

    2009-07-01

    Full Text Available In this paper we present a new, non-pheromone-based test suite optimization approach inspired by the behavior of biological bees. Our proposed approach is based on ABC (Artificial Bee Colony Optimization which is motivated by the intelligent behavior of honey bees. In our proposed system, the sites are the nodes in the Software under Test (SUT, the artificial bees modify the test cases with time and the bee?s aim is to discover the places of nodes with higher coverage and finally the one with the highest usage by the given test case. Since ABC system combines local search methods carried out by employed bees with global search methods managed by onlookers and scouts, we attain near global optima. We investigate whether this new approach outperforms existing test optimization approach based on Genetic Algorithms (GA in the task of software test optimization. Taking into account the results of our experiments, we conclude that (i the proposed approach uses fewer iterations to complete the task; (ii is more scalable, i.e., it requires less computation time to complete the task, and finally (iii our approach is best in achieving near global optimal solution.

  3. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning forneuromorphic systems

    Directory of Open Access Journals (Sweden)

    Zhongqiang eWang

    2015-01-01

    Full Text Available Resistive (or memristive switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i the natural variability of set/reset processes in the nanoscale switch, and (ii the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

  4. SCREEN Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

    CERN Document Server

    Wermter, S; Wermter, Stefan; Weber, Volker

    1997-01-01

    In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processi...

  5. Approaches to Learning to Control Dynamic Uncertainty

    Directory of Open Access Journals (Sweden)

    Magda Osman

    2015-10-01

    Full Text Available In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains or exploit (maximizing their short term gains? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

  6. A biomimetic approach for synthesizing artificial light-harvesting systems using self-assembly

    Energy Technology Data Exchange (ETDEWEB)

    Bhise, A.D.

    2005-10-01

    Photosynthesis is an extremely important process on Earth as it is the only natural source of food and fossil fuel, which fulfil our daily needs. After a certain period, the natural source of food and energy will decrease due to rapid consumption. Therefore, future generations will require alternative food and fuel sources. This represents a strong driving force to do research in construction of artificial light-harvesting (or antenna) systems. Synthetic antennas can be achieved either by covalent or non-covalent approaches by employing different strategies. This work throws light on the non-covalent approach i.e. a supramolecular approach in quest of artificial antenna systems wherein self-assembly and self-aggregation are at the focus. Furthermore this approach is biomimetic in nature as it is inspired by the antenna system which operates in green photosynthetic bacteria. Bacteriochlorophyll-c, d and e were selected as models for the syntheses of artificial mimics. The supramolecular interactions which are, the ligation of the central Mg atom by the 3{sup 1}-hydroxy group of another molecule; cooperative hydrogen bonding of the same OH group to the 13{sup 1}-carbonyl group of a third BChl-c molecule; and {pi}-{pi} interactions between the macrocycles are responsible for self-assembly of the building blocks or tectons. Well-defined architectures of self-assembling porphyrins find applications in mimicking the functions of light-harvesting. Porphyrins that are equipped with the same functional groups that are responsible for the self-assembly of bacteriochlorophylls-c, d and e within the chlorosomal antenna of some green photosynthetic bacteria, have been selectively synthesized from easily available and cheap starting materials, 10,20-Bis(3,5-di-t-butylphenyl)porphinato copper. All the target compounds were obtained after four to eight synthetic steps in good yields by employing different synthetic procedures involving also novel reactions. These fully synthetic

  7. AN ARTIFICIAL INTELLIGENCE APPROACH FOR THE PREDICTION OF SURFACE ROUGHNESS IN CO2 LASER CUTTING

    Directory of Open Access Journals (Sweden)

    MILOŠ MADIĆ

    2012-12-01

    Full Text Available In laser cutting, the cut quality is of great importance. Multiple non-linear effects of process parameters and their interactions make very difficult to predict cut quality. In this paper, artificial intelligence (AI approach was applied to predict the surface roughness in CO2 laser cutting. To this aim, artificial neural network (ANN model of surface roughness was developed in terms of cutting speed, laser power and assist gas pressure. The experimental results obtained from Taguchi’s L25 orthogonal array were used to develop ANN model. The ANN mathematical model of surface roughness was expressed as explicit nonlinear function of the selected input parameters. Statistical results indicate that the ANN model can predict the surface roughness with good accuracy. It was showed that ANNs may be used as a good alternative in analyzing the effects of cutting parameters on the surface roughness.

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

    International Nuclear Information System (INIS)

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

  9. Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

    CERN Document Server

    Mehrkesh, Amirhossein

    2014-01-01

    Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colorado River to such parameters as the amount of precipitation, ambient temperature and snowpack level at a specific time of the year. The model was able to precisely study the impact of climatic parameters on the flow rate of the Colorado River.

  10. A two-level on-line learning algorithm of Artificial Neural Network with forward connections

    Directory of Open Access Journals (Sweden)

    Stanislaw Placzek

    2014-12-01

    Full Text Available An Artificial Neural Network with cross-connection is one of the most popular network structures. The structure contains: an input layer, at least one hidden layer and an output layer. Analysing and describing an ANN structure, one usually finds that the first parameter is the number of ANN’s layers. A hierarchical structure is a default and accepted way of describing the network. Using this assumption, the network structure can be described from a different point of view. A set of concepts and models can be used to describe the complexity of ANN’s structure in addition to using a two-level learning algorithm. Implementing the hierarchical structure to the learning algorithm, an ANN structure is divided into sub-networks. Every sub-network is responsible for finding the optimal value of its weight coefficients using a local target function to minimise the learning error. The second coordination level of the learning algorithm is responsible for coordinating the local solutions and finding the minimum of the global target function. In the article a special emphasis is placed on the coordinator’s role in the learning algorithm and its target function. In each iteration the coordinator has to send coordination parameters into the first level of subnetworks. Using the input X and the teaching Z vectors, the local procedures are working and finding their weight coefficients. At the same step the feedback information is calculated and sent to the coordinator. The process is being repeated until the minimum of local target functions is achieved. As an example, a two-level learning algorithm is used to implement an ANN in the underwriting process for classifying the category of health in a life insurance company.

  11. Pedagogical quality in e-learning - Designing e-learning from a learning theoretical approach

    Directory of Open Access Journals (Sweden)

    Christian Dalsgaard

    2005-02-01

    Full Text Available The article is concerned with design and use of e-learning technology to develop education qualitatively. The purpose is to develop a framework for a pedagogical evaluation of e-learning technology. The approach is that evaluation and design must be grounded in a learning theoretical approach, and it is argued that it is necessary to make a reflection of technology in relation to activities, learning principles, and a learning theory in order to qualitatively develop education. The article presents three frameworks developed on the basis of cognitivism, radical constructivism and activity theory. Finally, on the basis of the frameworks, the article discusses e-learning technology and, more specifically, design of virtual learning environments and learning objects. It is argued that e-learning technology is not pedagogically neutral, and that it is therefore necessary to focus on design of technology that explicitly supports a certain pedagogical approach. Further, it is argued that design should direct its focus away from organisation of content and towards design of activities.

  12. A Conceptual Model of Relationships among Constructivist Learning Environment Perceptions, Epistemological Beliefs, and Learning Approaches

    Science.gov (United States)

    Ozkal, Kudret; Tekkaya, Ceren; Cakiroglu, Jale; Sungur, Semra

    2009-01-01

    This study proposed a conceptual model of relationships among constructivist learning environment perception variables (Personal Relevance, Uncertainty, Critical Voice, Shared Control, and Student Negotiation), scientific epistemological belief variables (fixed and tentative), and learning approach. It was proposed that learning environment…

  13. SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

    CERN Document Server

    Wermter, S

    2008-01-01

    Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken- language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, lea...

  14. A pedagogical approach to the design of Learning Objects

    OpenAIRE

    Busetti, Emanuela; Dettori, Giuliana; Forcheri, Paola; Ierardi, Maria Grazia

    2005-01-01

    In this paper we describe an approach to the design of learning objects (LOs) suitable to support learning in complex domains at university level. Our proposal is centred on a costructivist approach where learning is viewed as resulting from personal activity and comparison with the activity of others. Our pedagogical approach to knowledge acquisition and to the use of technological tools is realized by means of didactical units which can be implemented as Learning Objects (LOs) with a variet...

  15. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID

    Science.gov (United States)

    Liu, Jie; Zio, Enrico

    2016-08-01

    Support Vector Regression (SVR) is a popular supervised data-driven approach for building empirical models from available data. Like all data-driven methods, under non-stationary environmental and operational conditions it needs to be provided with adaptive learning capabilities, which might become computationally burdensome with large datasets cumulating dynamically. In this paper, a cost-efficient online adaptive learning approach is proposed for SVR by combining Feature Vector Selection (FVS) and Incremental and Decremental Learning. The proposed approach adaptively modifies the model only when different pattern drifts are detected according to proposed criteria. Two tolerance parameters are introduced in the approach to control the computational complexity, reduce the influence of the intrinsic noise in the data and avoid the overfitting problem of SVR. Comparisons of the prediction results is made with other online learning approaches e.g. NORMA, SOGA, KRLS, Incremental Learning, on several artificial datasets and a real case study concerning time series prediction based on data recorded on a component of a nuclear power generation system. The performance indicators MSE and MARE computed on the test dataset demonstrate the efficiency of the proposed online learning method.

  16. Social learning in Models and Cases - an Interdisciplinary Approach

    Science.gov (United States)

    Buhl, Johannes; De Cian, Enrica; Carrara, Samuel; Monetti, Silvia; Berg, Holger

    2016-04-01

    Our paper follows an interdisciplinary understanding of social learning. We contribute to the literature on social learning in transition research by bridging case-oriented research and modelling-oriented transition research. We start by describing selected theories on social learning in innovation, diffusion and transition research. We present theoretical understandings of social learning in techno-economic and agent-based modelling. Then we elaborate on empirical research on social learning in transition case studies. We identify and synthetize key dimensions of social learning in transition case studies. In the following we bridge between more formal and generalising modelling approaches towards social learning processes and more descriptive, individualising case study approaches by interpreting the case study analysis into a visual guide on functional forms of social learning typically identified in the cases. We then try to exemplarily vary functional forms of social learning in integrated assessment models. We conclude by drawing the lessons learned from the interdisciplinary approach - methodologically and empirically.

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

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-01-01

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

  18. Course Management Systems and Blended Learning: An Innovative Learning Approach

    Science.gov (United States)

    Chou, Amy Y.; Chou, David C.

    2011-01-01

    This article utilizes Rogers' innovation-decision process model (2003) and Beckman and Berry's innovation process model (2007) to create an innovative learning map that illustrates three learning methods (i.e., face-to-face learning, online learning, and blended learning) in two types of innovation (i.e., incremental innovation and radical…

  19. Component-Based Approach in Learning Management System Development

    Science.gov (United States)

    Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

    2013-01-01

    The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

  20. Concept Based Approach for Adaptive Personalized Course Learning System

    Science.gov (United States)

    Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali

    2013-01-01

    One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…

  1. A NOVEL APPROACH FOR SELECTION OF LEARNING OBJECTS FOR PERSONALIZED DELIVERY OF E-LEARNING CONTENT

    OpenAIRE

    Anitha, D; C. Deisy

    2013-01-01

    Personalized E-learning, as an intelligent package of technology enhanced education tends to overrule the traditional practices of static web based E-learning systems. Delivering suitable learning objects according to the learners’ knowledge, preferences and learning styles makes up the personalized E-learning. This paper proposes a novel approach for classifying and selecting learning objects for different learning styles proposed by Felder and Silverman The methodology adhere...

  2. Integrating Adult Learning and Technologies for Effective Education: Strategic Approaches

    Science.gov (United States)

    Wang, Victor C. X.

    2010-01-01

    As adult learners and educators pioneer the use of technology in the new century, attention has been focused on developing strategic approaches to effectively integrate adult learning and technology in different learning environments. "Integrating Adult Learning and Technologies for Effective Education: Strategic Approaches" provides innovative…

  3. A NEW RULE-BASED APPROACH FOR COMPUTER CHESS PROGRAMMING USING GP-ARTIFICIAL TECHNIQUES : PECP

    OpenAIRE

    HANEDAN, Y.Güney; SERTBAŞ, Ahmet

    2012-01-01

    In this paper, we use a brand new chess engine programming technique which we name PECP (Positional Evolutionary Chess Programming), that brings the Artificial Intelligence and Genetic Programming approaches together, to construct a chess endgame analyzing engine. Throughout the paper, the technique and the algorithm are discussed in detail. Also,  using PECP, an  example program (RETI V1.0)  aimed to prove the correctness and performance of the rule-based theory and algorithm is written in P...

  4. The relationship between strategic control and conscious structural knowledge in artificial grammar learning.

    Science.gov (United States)

    Norman, Elisabeth; Scott, Ryan B; Price, Mark C; Dienes, Zoltan

    2016-05-01

    We address Jacoby's (1991) proposal that strategic control over knowledge requires conscious awareness of that knowledge. In a two-grammar artificial grammar learning experiment all participants were trained on two grammars, consisting of a regularity in letter sequences, while two other dimensions (colours and fonts) varied randomly. Strategic control was measured as the ability to selectively apply the grammars during classification. For each classification, participants also made a combined judgement of (a) decision strategy and (b) relevant stimulus dimension. Strategic control was found for all types of decision strategy, including trials where participants claimed to lack conscious structural knowledge. However, strong evidence of strategic control only occurred when participants knew or guessed that the letter dimension was relevant, suggesting that strategic control might be associated with - or even causally requires - global awareness of the nature of the rules even though it does not require detailed knowledge of their content. PMID:27055066

  5. Lessons Learned from MAVRIC's Brain: An Anticipatory Artificial Agent and Proto-consciousness

    Science.gov (United States)

    Mobus, George

    2002-09-01

    MAVRIC II is a mobile, autonomous robot whose brain is comprised almost entirely of artificial adaptrode-based neurons. These neurons were previously shown to encode anticipatory actions. The architecture of this brain is based on the Extended Braitenberg Architecture (EBA). We are still in the process of collecting hard data on the behavioral traits of MAVRIC in the generalized foraging search task. But even now sufficient qualitative aspects of MAVRIC's behavior have been garnered from foraging experiments to lend strong support to the theory that MAVRIC is a highly adaptive, life-like agent. The development of the current MAVRIC brain has led to some important insights into the nature of intelligent control. In this paper we elucidate some of these principles in the form of lessons learned, and project the potential for future developments.

  6. Learning approaches in accounting education: Towards deep learning

    Directory of Open Access Journals (Sweden)

    Yeng Wai Lau

    2015-09-01

    Full Text Available Deep learning facilitates development of generic skills pertinent to prepare graduates for employment. Accounting education with syllabuses burdened with accounting standards to be memorized and regurgitated in examinations does little to promote deep learning. This study conducted a questionnaire survey to examine the extent to which accounting undergraduates at a public university in Malaysia adopt deep learning. This study demonstrates that deep learning is not readily attainable. Surface learning, which promotes rote memorization, constitutes a stepping stone towards deep learning. Having a preference or thirst for meanings is also pertinent to motivate undergraduates to move from rote memorization to seek meanings and thus deep learning. Female undergraduates have been found to be more inclined to adopt deep learning. Much is still to be learned on how best to promote deep learning as learning is a life-long process where everyday life experiences, both on and off-campus, facilitate learning and development.

  7. The Effects of Textisms on Learning, Study Time, and Instructional Perceptions in an Online Artificial Intelligence Instructional Module

    Science.gov (United States)

    Beasley, Robert; Bryant, Nathan L.; Dodson, Phillip T.; Entwistle, Kevin C.

    2013-01-01

    The purpose of this study was to investigate the effects of textisms (i.e., abbreviated spellings, acronyms, and other shorthand notations) on learning, study time, and instructional perceptions in an online artificial intelligence instructional module. The independent variable in this investigation was experimental condition. For the control…

  8. A genetic-neural artificial intelligence approach to resins optimization; Uma metodologia baseada em inteligencia artificial para otimizacao de resinas

    Energy Technology Data Exchange (ETDEWEB)

    Cabral, Denise C.; Barros, Marcio P.; Lapa, Celso M.F.; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: lapa@ien.gov.br; mbarros@ien.gov.br

    2005-07-01

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  9. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

    Science.gov (United States)

    Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  10. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    Science.gov (United States)

    Dülger, L. Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  11. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242

    Directory of Open Access Journals (Sweden)

    Ahmed R. J. Almusawi

    2016-01-01

    Full Text Available This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.

  12. Deep Multi-task Representation Learning: A Tensor Factorisation Approach

    OpenAIRE

    Yang, Yongxin; Hospedales, Timothy

    2016-01-01

    Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end...

  13. Robot's Behavioral Learning Based on Artificial Emotion and CMAC Network%基于人工情感与CMAC网络的机器人行为学习

    Institute of Scientific and Technical Information of China (English)

    祝宇虹; 魏金海

    2012-01-01

    人工情感是一个新兴的研究方向,情感智能是人工智能对人类智能的重要逼近.该文利用模糊情感模型来指导机器人学习,在CMAC网络框架内实现机器人的新的行为学习方式.仿真实验结果表明基于人工情感与CMAC网络的机器人行为学习能够学习到一条好的行为策略,具有良好的学习性能.该方法对于提高机器人在恶劣环境下的生存能力和自主决策能力具有很大理论意义和实际应用价值.%Artificial emotion is a new and developing research area,affective intelligence is an important approach to human intelligence. This paper uses a fuzzy artificial emotion mode! Which is used for guiding robot behavior scheme learning and it is to realize a new behavior schem learning method in the frame of CMAC network. The result of simulation tells that the robot behavior scheme learning based on artificial emotion and CMAC network can generate a sheme good enough and the learning is of good performance. This method has great theoretical significance and high application value for enhancing robot' s survivability and autonomous decision - making capacity in harsh and complex environment.

  14. Cyber Asynchronous versus Blended Cyber Approach in Distance English Learning

    Science.gov (United States)

    Ge, Zi-Gang

    2012-01-01

    This study aims to compare the single cyber asynchronous learning approach with the blended cyber learning approach in distance English education. Two classes of 70 students participated in this study, which lasted one semester of about four months, with one class using the blended approach for their English study and the other only using the…

  15. The Relationship between Intelligence, Approaches to Learning and Academic Achievement.

    Science.gov (United States)

    Diseth, Age

    2002-01-01

    Administered three tests of intelligence and the Approaches and Study Skills Inventory for Students (Entwhistle, 1997) to 89 Norwegian undergraduates to study the relationships among intelligence, approaches of learning, and academic achievement. Findings support the construct validity of approaches to learning because of its independence from…

  16. Delaunay variables approach to the elimination of the perigee in Artificial Satellite Theory

    Science.gov (United States)

    Lara, Martin; San-Juan, Juan F.; López-Ochoa, Luis M.

    2014-09-01

    Analytical integration in Artificial Satellite Theory may benefit from different canonical simplification techniques, like the elimination of the parallax, the relegation of the nodes, or the elimination of the perigee. These techniques were originally devised in polar-nodal variables, an approach that requires expressing the geopotential as a Pfaffian function in certain invariants of the Kepler problem. However, it has been recently shown that such sophisticated mathematics are not needed if implementing both the relegation of the nodes and the parallax elimination directly in Delaunay variables. Proceeding analogously, it is shown here how the elimination of the perigee can be carried out also in Delaunay variables. In this way the construction of the simplification algorithm becomes elementary, on one hand, and the computation of the transformation series is achieved with considerable savings, on the other, reducing the total number of terms of the elimination of the perigee to about one third of the number of terms required in the classical approach.

  17. Two-C but not two-V: Segment similarity in learning an artificial lexicon

    Science.gov (United States)

    Creel, Sarah C.; Aslin, Richard N.; Tanenhaus, Michael K.

    2003-10-01

    The role of segment similarity (C1-C2-or -V1-V2) in a word learning task was assessed using an artificial lexicon in a referential context. Learning consisted of 480 trials in which S's heard one of 40 CVCV nonsense strings, accompanied by an unfamiliar picture. In testing, participants heard the direction ``Click on the [nonsense word],'' and chose one of four pictures that matched the test item. On some trials, target lexical items (pibo) appeared with foils that contained matched consonants (pabu) or matched vowels (diko). There were higher rates of confusion errors to the matched-consonant items than to non-matched items, but no significant elevation in errors to matched-vowel items. A second experiment examined the role of differences in informativeness between C's and V's by inverting the numbers of C and V types (first experiment: 10 C, 5 V; second experiment: 5 C, 10 V). This made the consonants less predictive of word identity (more words contained the same consonants), and made the vowels more predictive of word identity. The matched-consonant effect remained undiminished while no corresponding matched-vowel effect emerged, ruling out a segment-informativeness explanation. Other accounts based on the syllable positions and confusability patterns of consonants are being explored.

  18. The Anatomic Study on Replacement of Artificial Atlanto-odontoid Joint through Transoral Approach

    Institute of Scientific and Technical Information of China (English)

    Yong Hu; Shuhua YANG; Hui XIE; Xianfeng HE; Rongming XU; Weihu MA; Jianxiang FENG; Qiu CHEN

    2008-01-01

    In order to provide anatomical basis for transoral approach (TOA) in dealing with the ventro lesions of craniocervical junction, and the design and application of artificial atlanto-odontoid joint, microsurgical dissecting was performed on 8 fresh craniocervical specimens layer by layer through transoropharyngeal approach. The stratification of posterior pharyngeal wall, course of vertebral artery, adjacent relationship of atlas and axis and correlative anatomical parameters of replacement of artificial atlanto-odontoid joint were observed. Besides, 32 sets of atlanto-axial joint in adults' fresh bony specimens were measured with a digital caliper and a goniometer, including the width of bony window of anterior arch of atlas, the width of bony window of axis vertebra, the distance between superior and inferior two atlas screw inserting points, the distance between two axis screw inserting points etc. It was found that the width of atlas and axis which could be exposed were 40.2±3.5mm and 39.3±3.7mm respectively. The width and height of posterior pharyngeal wall which could be exposed were 40.1±5.2mm and 50.2±4.6mm respectively. The distance between superior and inferior two atlas screw inserting points was 28.0±2.9mm and 24.0±3.5mm respectively, and the distance of bilateral axis screw inserting points was 18.0±1.2mm. The operative exposure position through TOA ranged from inferior part of the clivus to the superior part of the C3 vertebral body. Posterior pharyngeal wall consisted of 5 layers and two interspaces: mucosa, submucosa, superficial muscular layer, anterior fascia of vertebrae, anterior muscular layer of vertebrae and posterior interspace of pharynx, anterior interspace of vertebrae. This study revealed that it had the advantages of short operative distance, good exposure and sufficient decompression in dealing with the ventro lesions from the upper cervical to the lower clivus through the TOA. The replacement of artificial atlanto-odontoid joint

  19. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  20. A complementary approach to lifelong learning strategies

    Directory of Open Access Journals (Sweden)

    Galina Kavaliauskiené

    2009-10-01

    Full Text Available This paper examines how the language learning strategies that learners prefer in learning professional language at tertiary level can be used for lifelong education. It is well known that when learning a language learners use various learning strategies, but not all learners are equally successful in their studies. This research is based on the analysis of data obtained from two different surveys of learners’ preferred language learning strategies. Respondents spread over two levels of English proficiency and their learning strategies are compared. Self-evaluation and reflections on learning outcomes reveal how important or unimportant various learning strategies are and which might be relevant to lifelong learning. The study found that learners’ preferred individual strategies can be an effective way to foster their motivation for self-development and, in the long run, lifelong learning.

  1. An artificial immune system approach with secondary response for misbehavior detection in mobile ad hoc networks.

    Science.gov (United States)

    Sarafijanović, Slavisa; Le Boudec, Jean-Yves

    2005-09-01

    In mobile ad hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as dynamic source routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper, we investigate the use of an artificial immune system (AIS) to detect node misbehavior in a mobile ad hoc network using DSR. The system is inspired by the natural immune system (IS) of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns, and detects new misbehavior. We describe our solution for the classification task of the AIS; it employs negative selection and clonal selection, the algorithms for learning and adaptation used by the natural IS. We define how we map the natural IS concepts such as self, antigen, and antibody to a mobile ad hoc network and give the resulting algorithm for classifying nodes as misbehaving. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters affect the performance of primary and secondary response. Further steps will extend the design by using an analogy to the innate system, danger signal, and memory cells. PMID:16252818

  2. Development of an Artificial Intelligence Programming Course and Unity3d Based Framework to Motivate Learning in Artistic Minded Students

    DEFF Research Database (Denmark)

    Reng, Lars

    2012-01-01

    between technical and artistic minded students is, however, increased once the students reach the sixth semester. The complex algorithms of the artificial intelligence course seemed to demotivate the artistic minded students even before the course began. This paper will present the extensive changes made...... to the sixth semester artificial intelligence programming course, in order to provide a highly motivating direct visual feedback, and thereby remove the steep initial learning curve for artistic minded students. The framework was developed with close dialog to both the game industry and experienced master...

  3. Learning approaches and studies of effect of environmental factors

    Directory of Open Access Journals (Sweden)

    Mirkov Snežana

    2009-01-01

    Full Text Available There is a presentation of 3P model of learning (Presage-Process-Product, which comprises learning approaches placed in a wider context of the set of variables related to personality, environment, process and outcomes of learning. Three approaches to learning - surface, deep and achievement-oriented - consist of motives and the corresponding learning strategies. There is a discussion of the findings and implications of a great deal of research using the instruments based on this model. We analyzed research findings about the effect of instruction on learning approaches acquired by pupils, and especially students. It is shown how based on learning approach employed by pupils it is possible to draw conclusions about the quality of instruction. Testing the instruments on various samples indicates that the model is applicable in different cultures. Cross-cultural research opened up the problem of relation between memorising and understanding. Further research is necessary, both empirical and theoretical, that is, development of conceptualization of these constructs, and especially their role in education. Perspectives for further research also open up in the direction of studying various factors connected with personality and their relations with learning approaches. The role of learning approaches of teachers in developing the learning approaches of pupils is yet to be examined.

  4. Learning approaches in accounting education: Towards deep learning

    OpenAIRE

    Yeng Wai Lau; Shi Yee Lim

    2015-01-01

    Deep learning facilitates development of generic skills pertinent to prepare graduates for employment. Accounting education with syllabuses burdened with accounting standards to be memorized and regurgitated in examinations does little to promote deep learning. This study conducted a questionnaire survey to examine the extent to which accounting undergraduates at a public university in Malaysia adopt deep learning. This study demonstrates that deep learning is not readily attainable. Surface ...

  5. Web-based e-learning and virtual lab of human-artificial immune system.

    Science.gov (United States)

    Gong, Tao; Ding, Yongsheng; Xiong, Qin

    2014-05-01

    Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students.

  6. Web-based e-learning and virtual lab of human-artificial immune system.

    Science.gov (United States)

    Gong, Tao; Ding, Yongsheng; Xiong, Qin

    2014-05-01

    Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students. PMID:24816704

  7. Enhancing Students' Approaches to Learning: The Added Value of Gradually Implementing Case-Based Learning

    Science.gov (United States)

    Baeten, Marlies; Dochy, Filip; Struyven, Katrien

    2013-01-01

    Previous research has shown the difficulty of enhancing students' approaches to learning, in particular the deep approach, through student-centred teaching methods such as problem- and case-based learning. This study investigates whether mixed instructional methods combining case-based learning and lectures have the power to enhance students'…

  8. Do Learning Approaches of Medical Students Affect Their Satisfaction with Problem-Based Learning?

    Science.gov (United States)

    Gurpinar, Erol; Kulac, Esin; Tetik, Cihat; Akdogan, Ilgaz; Mamakli, Sumer

    2013-01-01

    The aim of this research was to determine the satisfaction of medical students with problem-based learning (PBL) and their approaches to learning to investigate the effect of learning approaches on their levels of satisfaction. The study group was composed of medical students from three different universities, which apply PBL at different levels…

  9. A New Design Approach to Game-Based learning

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    2012-01-01

    This paper puts forward a new design perspective for gamebased learning. The general idea is to abandon the long sought-after dream of designing a closed learning system, where students in both primary and secondary school could learn – without the interference of teachers – whatever subject......-based learning system, but will also confront aspects of modern learning theory, especially the notion of reference between the content of an assignment and the reality with which it should or could be connected (situated learning). The second idea promotes a way of tackling the common experience of the average...... to ground the student’s reason to learn. This paper proposes a different approach: using visualisation in immersive 3D worlds as both documentation of learning progress and as a reward system which motivates further learning. The overall design idea is to build a game based learning system from three...

  10. Do Humans Really Learn A[superscript n] B[superscript n] Artificial Grammars from Exemplars?

    Science.gov (United States)

    Hochmann, Jean-Remy; Azadpour, Mahan; Mehler, Jacques

    2008-01-01

    An important topic in the evolution of language is the kinds of grammars that can be computed by humans and other animals. Fitch and Hauser (F&H; 2004) approached this question by assessing the ability of different species to learn 2 grammars, (AB)[superscript n] and A[superscript n] B[superscript n]. A[superscript n] B[superscript n] was taken to…

  11. Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

    OpenAIRE

    Cresswell, Stephen; Prigmore, Martyn

    2008-01-01

    This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game.

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

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

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

  13. Discuss Optimal Approaches to Learning Strategy Instruction for EFL Learners

    Institute of Scientific and Technical Information of China (English)

    邢菊如

    2009-01-01

    Numerous research studies reveal that learning strategies have played an important role in language learning processes.This paper explores as English teachers.can we impmve students' language proficiency by giving them optimal learning strategy instruction and what approaches are most effective and efficient?

  14. A Learning Progressions Approach to Early Algebra Research and Practice

    Science.gov (United States)

    Fonger, Nicole L.; Stephens, Ana; Blanton, Maria; Knuth, Eric

    2015-01-01

    We detail a learning progressions approach to early algebra research and how existing work around learning progressions and trajectories in mathematics and science education has informed our development of a four-component theoretical framework consisting of: a curricular progression of learning goals across big algebraic ideas; an instructional…

  15. Curriculum Design Requirements and Challenges of the Learning Society Approach

    Science.gov (United States)

    Karimi, Sedighe; Nasr, Ahmad-Reza; Sharif, Mostafa

    2012-01-01

    Entering the twenty-first century with the development of communities, they are faced with the necessity of moving towards a learning society. University must extend the learning opportunities and improve the quality of them with curriculum design by learning society approach to respond to the necessity. Researchers believe that some conditions…

  16. Artificial neural network approach for moiré fringe center determination

    Science.gov (United States)

    Woo, Wing Hon; Ratnam, Mani Maran; Yen, Kin Sam

    2015-11-01

    The moiré effect has been used in high-accuracy positioning and alignment systems for decades. Various methods have been proposed to identify and locate moiré fringes in order to relate the pattern information to dimensional and displacement measurement. These methods can be broadly categorized into manual interpretation based on human knowledge and image processing based on computational algorithms. An artificial neural network (ANN) is proposed to locate moiré fringe centers within circular grating moiré patterns. This ANN approach aims to mimic human decision making by eliminating complex mathematical computations or time-consuming image processing algorithms in moiré fringe recognition. A feed-forward backpropagation ANN architecture was adopted in this work. Parametric studies were performed to optimize the ANN architecture. The finalized ANN approach was able to determine the location of the fringe centers with average deviations of 3.167 pixels out of 200 pixels (≈1.6%) and 6.166 pixels out of 200 pixels (≈3.1%) for real moiré patterns that lie within and outside the training intervals, respectively. In addition, a reduction of 43.4% in the computational time was reported using the ANN approach. Finally, the applicability of the ANN approach for moiré fringe center determination was confirmed.

  17. AN ARTIFICIAL NEURAL NETWORK APPROACH – PERFORMANCE MEASURE OF A RE-ENTRANT LINE IN A REFLOW SCREENING OPERATION

    Directory of Open Access Journals (Sweden)

    SURESH KUMAR

    2010-12-01

    Full Text Available This paper presents an artificial neural network (ANN method applied to a multistage re-entrant line system. Generally, queuing networks adopt analytical methods or use simulation packages to determine their performance measure. The contribution of this paper is the development of an alternate solution method using ANN approach to determine performance measure namely the total cycle time for a Reflow Screening (RS operation in a semiconductor assembly plant. Performance measure of an operation is an important aspect in management decision making. In order to validate the proposed method, comparison results were made using the analytical method based on mean value analysis (MVA technique for the re-entrant line and with some historical data collected from the operation. In this paper, Back Propagation Network (BPN learning algorithm is proposed for the computation of the total cycle time with respect to the number of lots circulating in the system. Extensive training and testing of the proposed ANN method is performed which enables the BPN model to be used to determine the required total cycle time.

  18. A Fuzzy Approach to Classify Learning Disability

    OpenAIRE

    Pooja Manghirmalani; Darshana More; Kavita Jain

    2012-01-01

    The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability...

  19. Blended learning approaches enhance student academic performance

    OpenAIRE

    Morris, NP

    2010-01-01

    Blended learning, or technology enhanced learning, is increasingly becoming an expectation for higher education students. Blended learning allows for the enhancement of face-to-face interaction between tutors and students, using internet or computer based tools. In this paper, a range of case studies are described which illustrate methods to engage students with technology enhanced learning and improve academic performance and student satisfaction. In the first case study, first year undergra...

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

    Directory of Open Access Journals (Sweden)

    Zdzislaw eKOWALCZUK

    2016-04-01

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

  1. NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    WANG Weizhong; ZHAO Jie; GAO Yongsheng; CAI Hegao

    2006-01-01

    A novel approach for collision-free path planning of a multiple degree-of-freedom (DOF)articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point (VNP), a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise fiom initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

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

    Science.gov (United States)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

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

  3. 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......In this paper, feedforward neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading...... via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general....

  4. An integrated data envelopment analysis-artificial neural network approach for benchmarking of bank branches

    Science.gov (United States)

    Shokrollahpour, Elsa; Hosseinzadeh Lotfi, Farhad; Zandieh, Mostafa

    2016-02-01

    Efficiency and quality of services are crucial to today's banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks. The drawback to it could be that the benchmarks it provides us with, may still be less efficient compared to the more advanced future benchmarks. To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches. Therefore, each branch could have a strategy to improve the efficiency and eliminate the cause of inefficiencies based on a 5-year time forecast.

  5. Generational diversity: teaching and learning approaches.

    Science.gov (United States)

    Johnson, Susan A; Romanello, Mary L

    2005-01-01

    Nursing students represent multiple generations--Baby Boomers, Generation X, and now the Millennials. Each generation has its own set of values, ideas, ethics, beliefs, and learning styles. The authors describe the context, characteristics, and learning styles of each generation and provide suggestions for enhanced teaching and learning across multiple generations. Using generational diversity as a teaching tool in the classroom is also discussed.

  6. Learning to Learn about Spirituality: A Categorical Approach To Introducing the Topic into Management Classes.

    Science.gov (United States)

    Barnett, Carole K.; Krell, Terence C.; Sendry, Jeanette

    2000-01-01

    Presents a typology of approaches to spiritual development based on spiritual path type (mystical, personal, ritual, group-participative, ecstatic). Includes a classroom exercise that enables students to identify their spiritual path and learn how to learn about spirituality. (SK)

  7. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

  8. An Efficient Dual Approach to Distance Metric Learning

    OpenAIRE

    Shen, Chunhua; Kim, Junae; Liu, Fayao; Wang, Lei; Hengel, Anton van den

    2013-01-01

    Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. Standard interior-point SDP solvers typically have a complexity of $O(D^{6.5})$ (with $D$ the dimension of input data), and can thus onl...

  9. Outdoor Education: An Alternative Approach in Teaching and Learning Science

    OpenAIRE

    Tuan Mastura Tuan Soh; Tamby Subahan Mohd. Meerah

    2013-01-01

    To understand fully and aware of children’s science learning, one should look not only at learning that takes place in the kindergarten and primary school but also in learning that takes place outside the classroom. This paper aims to discuss outdoor education: an alternative approach in teaching and learning science in the Malaysian context. In this 21st century, the exposure and experience in the field of science and technology are needed in nurturing interest among students who are involve...

  10. Students' Approaches to Learning a New Mathematical Model

    Science.gov (United States)

    Flegg, Jennifer A.; Mallet, Daniel G.; Lupton, Mandy

    2013-01-01

    In this article, we report on the findings of an exploratory study into the experience of undergraduate students as they learn new mathematical models. Qualitative and quantitative data based around the students' approaches to learning new mathematical models were collected. The data revealed that students actively adopt three approaches to…

  11. Enhancing the Teaching-Learning Process: A Knowledge Management Approach

    Science.gov (United States)

    Bhusry, Mamta; Ranjan, Jayanthi

    2012-01-01

    Purpose: The purpose of this paper is to emphasize the need for knowledge management (KM) in the teaching-learning process in technical educational institutions (TEIs) in India, and to assert the impact of information technology (IT) based KM intervention in the teaching-learning process. Design/methodology/approach: The approach of the paper is…

  12. A Guided Discovery Approach for Learning Metabolic Pathways

    Science.gov (United States)

    Schultz, Emeric

    2005-01-01

    Learning the wealth of information in metabolic pathways is both challenging and overwhelming for students. A step-by-step guided discovery approach to the learning of the chemical steps in gluconeogenesis and the citric acid cycle is described. This approach starts from concepts the student already knows, develops these further in a logical…

  13. Forward-Oriented Design for Learning: Illustrating the Approach

    Science.gov (United States)

    Dimitriadis, Yannis; Goodyear, Peter

    2013-01-01

    This paper concerns sustainable approaches to design for learning, emphasising the need for designs to be able to thrive outside of the protective niches of project-based innovation. It builds on the "in medias res" framework and more specifically on a forward-oriented approach to design for learning: one that takes a pro-active design…

  14. Water level forecasting through fuzzy logic and artificial neural network approaches

    Directory of Open Access Journals (Sweden)

    S. Alvisi

    2006-01-01

    Full Text Available In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy. It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.

  15. Approach to Learning of Sub-Degree Students in Hong Kong

    Science.gov (United States)

    Chan, Yiu Man; Chan, Christine Mei Sheung

    2010-01-01

    The learning approaches and learning experiences of 404 sub-degree students were assessed by using a Study Process Questionnaire and a Learning Experience Questionnaire. While the learning approaches in this study meant whether students used a deep learning or surface learning approach, the learning experiences referred to students' perceptions…

  16. DEVELOPING A MATH LEARNING ENVIRONMENT – A LEARNING OBJECT APPROACH

    OpenAIRE

    Stanica Justina Lavinia

    2011-01-01

    Implementing a software architecture, that provides the learning content in a dynamic manner, would allow educational developers to use the same content more than one time, at a very structured level. The concept underlying this architecture is that of Learning Objects, a promising technology, which allows the separation of data, logic and presentation levels, offering the potential for interoperability, combination and reusability. In this context, emerged the idea to define a learning objec...

  17. On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants

    Directory of Open Access Journals (Sweden)

    Nil Goksel Canbek

    2016-01-01

    Full Text Available In a technology dominated world, useful and timely information can be accessed quickly via Intelligent Personal Assistants (IPAs.  By the use of these assistants built into mobile operating systems, daily electronic tasks of a user can be accomplished 24/7. Such tasks like taking dictation, getting turn-by-turn directions, vocalizing email messages, reminding daily appointments, setting reminders, responding any factual questions and invoking apps can be completed by  IPAs such as Apple’s Siri, Google Now and Microsoft Cortana. The mentioned assistants programmed within Artificial Intelligence (AI do create an interaction between human and computer through a natural language used in digital communication. In this regard, the overall purpose of this study is to examine the potential use of IPAs that use advanced cognitive computing technologies and Natural Language Processing (NLP for learning. To achieve this purpose, the working system of IPAs is reviewed briefly within the scope of AI that has recently become smarter to predict, comprehend and carry out multi-step and complex requests of users.

  18. A New Design Approach to game or play based learning

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    Abstract: The present paper proposes a new design perspective for game based learning. The general idea is to abandon the long and sought after dream of designing a closed learning system, where students from elementary school to high school without teachers’ interference could learn whatever...... game based learning system, but also confront aspects of modern learning theory especially the notion of reference between content of an assignment and the reality with which it should or could be connected (situated learning). The second idea promotes a way to tackle the common experience...... to ground the students sense of meaning. This paper proposes another approach: using visualization in immersive 3D-worlds as documentation of learning progress while at the same time constituting a reward system which motivate further learning. The overall design idea is to build a game based learning...

  19. A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

    Directory of Open Access Journals (Sweden)

    Gaochao Xu

    2013-01-01

    Full Text Available Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

  20. A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

    Directory of Open Access Journals (Sweden)

    Zhongbin Wang

    2016-01-01

    Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

  1. A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

    Science.gov (United States)

    Xu, Gaochao; Hu, Liang; Fu, Xiaodong

    2013-01-01

    Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful. PMID:24385877

  2. A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm.

    Science.gov (United States)

    Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

    2016-01-01

    In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

  3. Students awareness of learning styles and their perceptions to a mixed method approach for learning

    OpenAIRE

    Bhagat, Anumeha; Vyas, Rashmi; Singh, Tejinder

    2015-01-01

    Background: Individualization of instructional method does not contribute significantly to learning outcomes although it is known that students have differing learning styles (LSs). Hence, in order to maximally enhance learning, one must try to use a mixed method approach. Hypothesis: Our hypothesis was that awareness of preferred LS and motivation to incorporate multiple learning strategies might enhance learning outcomes. Aim: Our aim was to determine the impact of awareness of LS among med...

  4. Measuring students’ approaches to learning in different clinical rotations

    Directory of Open Access Journals (Sweden)

    Emilia Ova

    2012-11-01

    Full Text Available Abstract Background Many studies have explored approaches to learning in medical school, mostly in the classroom setting. In the clinical setting, students face different conditions that may affect their learning. Understanding students’ approaches to learning is important to improve learning in the clinical setting. The aim of this study was to evaluate the Study Process Questionnaire (SPQ as an instrument for measuring clinical learning in medical education and also to show whether learning approaches vary between rotations. Methods All students involved in this survey were undergraduates in their clinical phase. The SPQ was adapted to the clinical setting and was distributed in the last week of the clerkship rotation. A longitudinal study was also conducted to explore changes in learning approaches. Results Two hundred and nine students participated in this study (response rate 82.0%. The SPQ findings supported a two-factor solution involving deep and surface approaches. These two factors accounted for 45.1% and 22.5%, respectively, of the variance. The relationships between the two scales and their subscales showed the internal consistency and factorial validity of the SPQ to be comparable with previous studies. The clinical students in this study had higher scores for deep learning. The small longitudinal study showed small changes of approaches to learning with different rotation placement but not statistically significant. Conclusions The SPQ was found to be a valid instrument for measuring approaches to learning among clinical students. More students used a deep approach than a surface approach. Changes of approach not clearly occurred with different clinical rotations.

  5. Utilizing an Artificial Outcrop to Scaffold Learning Between Laboratory and Field Experiences in a College-Level Introductory Geology Course

    Science.gov (United States)

    Wilson, Meredith

    Geologic field trips are among the most beneficial learning experiences for students as they engage the topic of geology, but they are also difficult environments to maximize learning. This action research study explored one facet of the problems associated with teaching geology in the field by attempting to improve the transition of undergraduate students from a traditional laboratory setting to an authentic field environment. Utilizing an artificial outcrop, called the GeoScene, during an introductory college-level non-majors geology course, the transition was studied. The GeoScene was utilized in this study as an intermediary between laboratory and authentic field based experiences, allowing students to apply traditional laboratory learning in an outdoor environment. The GeoScene represented a faux field environment; outside, more complex and tangible than a laboratory, but also simplified geologically and located safely within the confines of an educational setting. This exploratory study employed a mixed-methods action research design. The action research design allowed for systematic inquiry by the teacher/researcher into how the students learned. The mixed-methods approach garnered several types of qualitative and quantitative data to explore phenomena and support conclusions. Several types of data were collected and analyzed, including: visual recordings of the intervention, interviews, analytic memos, student reflections, field practical exams, and a pre/post knowledge and skills survey, to determine whether the intervention affected student comprehension and interpretation of geologic phenomena in an authentic field environment, and if so, how. Students enrolled in two different sections of the same laboratory course, sharing a common lecture, participated in laboratory exercises implementing experiential learning and constructivist pedagogies that focused on learning the basic geological skills necessary for work in a field environment. These laboratory

  6. Lifelong Learning in Architectural Design Studio: The Learning Contract Approach

    Science.gov (United States)

    Hassanpour, B.; Che-Ani, A. I.; Usman, I. M. S.; Johar, S.; Tawil, N. M.

    2015-01-01

    Avant-garde educational systems are striving to find lifelong learning methods. Different fields and majors have tested a variety of proposed models and found varying difficulties and strengths. Architecture is one of the most critical areas of education because of its special characteristics, such as learning by doing and complicated evaluation…

  7. Problem Finding in Professional Learning Communities: A Learning Study Approach

    Science.gov (United States)

    Tan, Yuen Sze Michelle; Caleon, Imelda Santos

    2016-01-01

    This study marries collaborative problem solving and learning study in understanding the onset of a cycle of teacher professional development process within school-based professional learning communities (PLCs). It aimed to explore how a PLC carried out collaborative problem finding--a key process involved in collaborative problem solving--that…

  8. IMPROVE AFFECTIVE LEARNING WITH EEG APPROACH

    OpenAIRE

    Xiaowei Li; Qinglin Zhao; Li Liu; Hong Peng; Yanbing Qi; Chengsheng Mao; Zheng Fang; Quanying Liu; Bin Hu

    2010-01-01

    With the development of computer science, cognitive science and psychology, a new paradigm, affective learning, has emerged into e-learning domain. Although scientists and researchers have achieved fruitful outcomes in exploring the ways of detecting and understanding learners affect, e.g. eyes motion, facial expression etc., it sounds still necessary to deepen the recognition of learners affect in learning procedure with innovative methodologies. Our research focused on using bio-signals bas...

  9. A collaborative learning approach and its evaluation

    OpenAIRE

    Ishitani, Lucila; Guimarães, Silvio J. F.; Bruegger, Gisele

    2006-01-01

    The use of new technologies does not mean that the applied education model is modern. New technologies can be used in a way that follows the traditional education model, with all its deficiencies. The collaborative education model involves students in reflection, participation, and construction of their knowledge, or to collaboratively learn. This article aims to present mechanisms to stimulate collaborative learning, in present education, through the aid of virtual learning environments.

  10. Generational diversity: teaching and learning approaches.

    Science.gov (United States)

    Johnson, Susan A; Romanello, Mary L

    2005-01-01

    Nursing students represent multiple generations--Baby Boomers, Generation X, and now the Millennials. Each generation has its own set of values, ideas, ethics, beliefs, and learning styles. The authors describe the context, characteristics, and learning styles of each generation and provide suggestions for enhanced teaching and learning across multiple generations. Using generational diversity as a teaching tool in the classroom is also discussed. PMID:16170263

  11. Does formal complexity reflect cognitive complexity? Investigating aspects of the Chomsky Hierarchy in an artificial language learning study.

    Science.gov (United States)

    Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

    2015-01-01

    This study investigated whether formal complexity, as described by the Chomsky Hierarchy, corresponds to cognitive complexity during language learning. According to the Chomsky Hierarchy, nested dependencies (context-free) are less complex than cross-serial dependencies (mildly context-sensitive). In two artificial grammar learning (AGL) experiments participants were presented with a language containing either nested or cross-serial dependencies. A learning effect for both types of dependencies could be observed, but no difference between dependency types emerged. These behavioral findings do not seem to reflect complexity differences as described in the Chomsky Hierarchy. This study extends previous findings in demonstrating learning effects for nested and cross-serial dependencies with more natural stimulus materials in a classical AGL paradigm after only one hour of exposure. The current findings can be taken as a starting point for further exploring the degree to which the Chomsky Hierarchy reflects cognitive processes. PMID:25885790

  12. Does formal complexity reflect cognitive complexity? Investigating aspects of the Chomsky Hierarchy in an artificial language learning study.

    Science.gov (United States)

    Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

    2015-01-01

    This study investigated whether formal complexity, as described by the Chomsky Hierarchy, corresponds to cognitive complexity during language learning. According to the Chomsky Hierarchy, nested dependencies (context-free) are less complex than cross-serial dependencies (mildly context-sensitive). In two artificial grammar learning (AGL) experiments participants were presented with a language containing either nested or cross-serial dependencies. A learning effect for both types of dependencies could be observed, but no difference between dependency types emerged. These behavioral findings do not seem to reflect complexity differences as described in the Chomsky Hierarchy. This study extends previous findings in demonstrating learning effects for nested and cross-serial dependencies with more natural stimulus materials in a classical AGL paradigm after only one hour of exposure. The current findings can be taken as a starting point for further exploring the degree to which the Chomsky Hierarchy reflects cognitive processes.

  13. A Fuzzy Approach to Classify Learning Disability

    Directory of Open Access Journals (Sweden)

    Pooja Manghirmalani

    2012-05-01

    Full Text Available The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability into its various types. This expert system facilitates in simulating conditions which are otherwise imprecisely defined.

  14. A PSO based Artificial Neural Network approach for short term unit commitment problem

    Directory of Open Access Journals (Sweden)

    AFTAB AHMAD

    2010-10-01

    Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.

  15. Undergraduate students' earth science learning: relationships among conceptions, approaches, and learning self-efficacy in Taiwan

    Science.gov (United States)

    Shen, Kuan-Ming; Lee, Min-Hsien; Tsai, Chin-Chung; Chang, Chun-Yen

    2016-06-01

    In the area of science education research, studies have attempted to investigate conceptions of learning, approaches to learning, and self-efficacy, mainly focusing on science in general or on specific subjects such as biology, physics, and chemistry. However, few empirical studies have probed students' earth science learning. This study aimed to explore the relationships among undergraduates' conceptions of, approaches to, and self-efficacy for learning earth science by adopting the structural equation modeling technique. A total of 268 Taiwanese undergraduates (144 females) participated in this study. Three instruments were modified to assess the students' conceptions of, approaches to, and self-efficacy for learning earth science. The results indicated that students' conceptions of learning made a significant contribution to their approaches to learning, which were consequently correlated with their learning self-efficacy. More specifically, students with stronger agreement that learning earth science involves applying the knowledge and skills learned to unknown problems were prone to possess higher confidence in learning earth science. Moreover, students viewing earth science learning as understanding earth science knowledge were more likely to adopt meaningful strategies to learn earth science, and hence expressed a higher sense of self-efficacy. Based on the results, practical implications and suggestions for future research are discussed.

  16. Novel reinforcement learning approach for difficult control problems

    Science.gov (United States)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  17. A Learning Approach to Optical Tomography

    CERN Document Server

    Shoreh, Morteza H; Papadopoulos, Ioannis N; Goy, Alexandre; Vonesch, Cedric; Unser, Michael; Psaltis, Demetri

    2015-01-01

    We describe a method for imaging 3D objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of cells.

  18. A medical expert system approach using artificial neural networks for standardized treatment planning

    International Nuclear Information System (INIS)

    Purpose: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. Methods and Materials: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. Results: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. Conclusions: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely

  19. Identification of time-varying structural dynamic systems - An artificial intelligence approach

    Science.gov (United States)

    Glass, B. J.; Hanagud, S.

    1992-01-01

    An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

  20. Dynamic Predictions from Time Series Data An Artificial Neural Network Approach

    CERN Document Server

    Kulkarni, D R; Parikh, J C

    1997-01-01

    A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model time series generated by complex dynamic systems. We introduce well known features used in the study of dynamic systems - time delay $\\tau$ and embedding dimension $d$ - for ANN modelling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture and leads to better predictions. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two...

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

    Science.gov (United States)

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

    2015-12-01

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

  2. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Directory of Open Access Journals (Sweden)

    Jing Shao

    Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  3. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Karin Kandananond

    2011-08-01

    Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance

  4. Child Development: An Active Learning Approach

    Science.gov (United States)

    Levine, Laura E.; Munsch, Joyce

    2010-01-01

    Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…

  5. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  6. Use of Blended Approach in the Learning of Electromagnetic Induction

    CERN Document Server

    Chew, Charles

    2015-01-01

    This paper traces the importance of pedagogical content knowledge in the digital age to prepare today students for the 21st century. It highlights the need for ICT-based pedagogical models that are grounded in both the learning theories of constructivism and connectivism. One such suitable ICT-based pedagogical model is the TSOI Hybrid Learning Model. By means of a physics blended learning exemplar based on the TSOI Hybrid Learning Model, this paper argues for the use of blended learning approach as the way forward for 21st century teaching.

  7. Multi-dimensional technology-enabled social learning approach

    DEFF Research Database (Denmark)

    Petreski, Hristijan; Tsekeridou, Sofia; Prasad, Neeli R.;

    2013-01-01

    in learning while socializing within their learning communities. However, their “educational” usage is still limited to facilitation of online learning communities and to collaborative authoring of learning material complementary to existing formal (e-) learning services. If the educational system doesn......’t respond to this systemic and structural changes and/or challenges and retains its status quo than it is jeopardizing its own existence or the existence of the education, as we know it. This paper aims to precede one step further by proposing a multi-dimensional approach for technology-enabled social...

  8. Machine Teaching -- A Machine Learning Approach to Technology Enhanced Learning

    OpenAIRE

    Weimer, Markus

    2010-01-01

    Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including informal knowledge such as experience and intuition that is key to many complex activities. We notice that knowledge, even if not standardized, structured and externalized,...

  9. A Team Approach to Successful Learning: Peer Learning Coaches in Chemistry

    Science.gov (United States)

    Popejoy, Kate; Asala, Kathryn S.

    2013-01-01

    High failure rates in introductory large lecture chemistry courses for STEM majors have been of concern for years. Through our weekly Team Approach to Successful Learning (TASL) workshops, students learn and apply problem-solving strategies, coached by specially trained peer learning coaches (LCs). These coaches concurrently enroll in Chemistry…

  10. Undergraduate Students' Earth Science Learning: Relationships among Conceptions, Approaches, and Learning Self-Efficacy in Taiwan

    Science.gov (United States)

    Shen, Kuan-Ming; Lee, Min-Hsien; Tsai, Chin-Chung; Chang, Chun-Yen

    2016-01-01

    In the area of science education research, studies have attempted to investigate conceptions of learning, approaches to learning, and self-efficacy, mainly focusing on science in general or on specific subjects such as biology, physics, and chemistry. However, few empirical studies have probed students' earth science learning. This study aimed to…

  11. An Expert System-based Context-Aware Ubiquitous Learning Approach for Conducting Science Learning Activities

    Science.gov (United States)

    Wu, Po-Han; Hwang, Gwo-Jen; Tsai, Wen-Hung

    2013-01-01

    Context-aware ubiquitous learning has been recognized as being a promising approach that enables students to interact with real-world learning targets with supports from the digital world. Several researchers have indicated the importance of providing learning guidance or hints to individual students during the context-aware ubiquitous learning…

  12. A first approach to “Learning Dashboards” in formal learning contexts

    NARCIS (Netherlands)

    Verpoorten, Dominique; Westera, Wim; Specht, Marcus

    2012-01-01

    Verpoorten, D., Westera, W., & Specht, M. (2011, 20 September). A first approach to “Learning Dashboards” in formal learning contexts. Paper presented at the ADVTEL workshop (1st International Workshop on Enhancing Learning with Ambient Displays and Visualization Techniques) at EC-TEL 2011, Palermo,

  13. Student-Centred Learning Environments: An Investigation into Student Teachers' Instructional Preferences and Approaches to Learning

    Science.gov (United States)

    Baeten, Marlies; Dochy, Filip; Struyven, Katrien; Parmentier, Emmeline; Vanderbruggen, Anne

    2016-01-01

    The use of student-centred learning environments in education has increased. This study investigated student teachers' instructional preferences for these learning environments and how these preferences are related to their approaches to learning. Participants were professional Bachelor students in teacher education. Instructional preferences and…

  14. Artificial sweeteners and metabolic dysregulation: Lessons learned from agriculture and the laboratory.

    Science.gov (United States)

    Shearer, Jane; Swithers, Susan E

    2016-06-01

    Escalating rates of obesity and public health messages to reduce excessive sugar intake have fuelled the consumption of artificial sweeteners in a wide range of products from breakfast cereals to snack foods and beverages. Artificial sweeteners impart a sweet taste without the associated energy and have been widely recommended by medical professionals since they are considered safe. However, associations observed in long-term prospective studies raise the concern that regular consumption of artificial sweeteners might actually contribute to development of metabolic derangements that lead to obesity, type 2 diabetes and cardiovascular disease. Obtaining mechanistic data on artificial sweetener use in humans in relation to metabolic dysfunction is difficult due to the long time frames over which dietary factors might exert their effects on health and the large number of confounding variables that need to be considered. Thus, mechanistic data from animal models can be highly useful because they permit greater experimental control. Results from animal studies in both the agricultural sector and the laboratory indicate that artificial sweeteners may not only promote food intake and weight gain but can also induce metabolic alterations in a wide range of animal species. As a result, simple substitution of artificial sweeteners for sugars in humans may not produce the intended consequences. Instead consumption of artificial sweeteners might contribute to increases in risks for obesity or its attendant negative health outcomes. As a result, it is critical that the impacts of artificial sweeteners on health and disease continue to be more thoroughly evaluated in humans. PMID:27387506

  15. Artificial sweeteners and metabolic dysregulation: Lessons learned from agriculture and the laboratory.

    Science.gov (United States)

    Shearer, Jane; Swithers, Susan E

    2016-06-01

    Escalating rates of obesity and public health messages to reduce excessive sugar intake have fuelled the consumption of artificial sweeteners in a wide range of products from breakfast cereals to snack foods and beverages. Artificial sweeteners impart a sweet taste without the associated energy and have been widely recommended by medical professionals since they are considered safe. However, associations observed in long-term prospective studies raise the concern that regular consumption of artificial sweeteners might actually contribute to development of metabolic derangements that lead to obesity, type 2 diabetes and cardiovascular disease. Obtaining mechanistic data on artificial sweetener use in humans in relation to metabolic dysfunction is difficult due to the long time frames over which dietary factors might exert their effects on health and the large number of confounding variables that need to be considered. Thus, mechanistic data from animal models can be highly useful because they permit greater experimental control. Results from animal studies in both the agricultural sector and the laboratory indicate that artificial sweeteners may not only promote food intake and weight gain but can also induce metabolic alterations in a wide range of animal species. As a result, simple substitution of artificial sweeteners for sugars in humans may not produce the intended consequences. Instead consumption of artificial sweeteners might contribute to increases in risks for obesity or its attendant negative health outcomes. As a result, it is critical that the impacts of artificial sweeteners on health and disease continue to be more thoroughly evaluated in humans.

  16. Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.

    Science.gov (United States)

    Ji, Junzhong; Liu, Jinduo; Liang, Peipeng; Zhang, Aidong

    2016-01-01

    Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith's simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity. PMID:27045295

  17. Holistic approaches to e-learning accessibility

    Directory of Open Access Journals (Sweden)

    Lawrie Phipps

    2006-12-01

    Full Text Available The importance of accessibility to digital e-learning resources is widely acknowledged. The World Wide Web Consortium Web Accessibility Initiative has played a leading role in promoting the importance of accessibility and developing guidelines that can help when developing accessible web resources. The accessibility of e-learning resources provides additional challenges. While it is important to consider the technical and resource related aspects of e-learning when designing and developing resources for students with disabilities, there is a need to consider pedagogic and contextual issues as well. A holistic framework is therefore proposed and described, which in addition to accessibility issues takes into account learner needs, learning outcomes, local factors, infrastructure, usability and quality assurance. The practical application and implementation of this framework is discussed and illustrated through the use of examples and case studies.

  18. A Goal-Based Approach for Learning in Business Processes

    Science.gov (United States)

    Soffer, Pnina; Ghattas, Johny; Peleg, Mor

    Organizations constantly strive to improve their business performance; hence they make business process redesign efforts. So far, redesign has mainly been a human task, which relies on human reasoning and creativity, although various analysis tools can support it by identifying improvement opportunities. This chapter proposes an automated approach for learning from accumulated experience and improving business processes over time. The approach ties together three aspects of business processes: goals, context, and actual paths. It proposes a learning cycle, including a learning phase, where the relevant context is identified and used for making improvements in the process model, and a runtime application phase, where the improved process model is applied at runtime and actual results are stored for the next learning cycle. According to our approach, a goal-oriented process model is essential for learning to improve process outcomes.

  19. Image-Based Learning Approach Applied to Time Series Forecasting

    OpenAIRE

    J. C. Chimal-Eguía; K. Ramírez-Amáro

    2012-01-01

    In this paper, a new learning approach based on time-series image information is presented. In order to implementthis new learning technique, a novel time-series input data representation is also defined. This input data representation is based on information obtained by image axis division into boxes. The difference between this new input data representation and the classical is that this technique is not time-dependent. This new information is implemented in the new Image-Based Learning A...

  20. Changing the learning environment to promote deep learning approaches in first year accounting students

    OpenAIRE

    Hall, Matthew; Ramsay, Alan; Raven, John

    2004-01-01

    Developing deep approaches to learning is claimed to enhance students' engagement with their subject material and result in improved analytical and conceptual thinking skills. Numerous calls have been made for accounting educators to adopt strategies that produce such results. This paper reports on changes to the learning environment centring on the introduction of group learning activities that were designed to improve the quality of students' learning outcomes. The impact of changes in the ...

  1. Towards an Adaptive Learning System Based on a New Learning Object Granularity Approach

    OpenAIRE

    Amal Battou; Ali El Mezouary; Driss Mammass

    2011-01-01

    To achieve the adaptability required in ALS, adaptive learning system (ALS) takes advantage of granular and reusable content. The main goal of this paper is to examine the learning object granularity issue which is directly related with Learning Object (LO) reusability and the adaptability process required in ALS. For that purpose, we present the learning objects approach and the related technologies. Then, we discuss the fine-grained as a fundamental characteristic to reach the adaptabil...

  2. Web-Based Teaching and Learning Approach (WBTLA) Usability in Institutions of Higher Learning in Malaysia

    Science.gov (United States)

    Nordin, Abu Bakar; Alias, Norlidah

    2013-01-01

    Today teachers in schools and lecturers in institutions of higher learning are endowed with a wide range of new teaching experiences through web-based teaching and learning approaches (WBTLA), which was not possible before through the traditional classroom approach. With the use of WBTLA emerged problems related to usability in technical,…

  3. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    OpenAIRE

    Habsah Ismail; Aminuddin Hassan; Mohd. Mokhtar Muhamad; Wan Zah Wan Ali; Mohd Majid Konting

    2013-01-01

    This is an investigation of the students’ beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items measuring students’ epistemological beliefs and 20 items on their learning approaches. Items on epistemological belief were adapted and modified from...

  4. Sound Foundations: Organic Approaches to Learning Notation in Beginning Band

    Science.gov (United States)

    West, Chad

    2016-01-01

    By starting with a foundation of sound before sight, we can help our students learn notation organically in a way that honors the natural process. This article describes five organic approaches to learning notation in beginning band: (1) iconic notation, (2) point and play, (3) student lead-sheet, (4) modeling, and (5) kid dictation. While…

  5. Learning and teaching as a game: A sabotage approach

    NARCIS (Netherlands)

    N. Gierasimczuk; L. Kurzen; F.R. Velázquez-Quesada

    2009-01-01

    In formal approaches to inductive learning, the ability to learn is understood as the ability to single out a correct hypothesis from a range of possibilities. Although most of the existing research focuses on the characteristics of the learner, in many paradigms the significance of the teacher’s ab

  6. Mini Anchors: A Universal Design for Learning Approach

    Science.gov (United States)

    Zydney, Janet Mannheimer; Hasselbring, Ted S.

    2014-01-01

    Teachers are challenged to create flexible learning environments that prepare students with diverse learning needs for adaptable thinking in a fast-paced and changing society. To address this need, we used a design-based research approach to develop a technology-based solution to individualize mathematical problem solving instruction to students…

  7. Motivational factors as predictors of student approach to learning

    DEFF Research Database (Denmark)

    Lassesen, Berit Irene

    Background and aim: Research indicates that both self-efficacy and test anxiety may influence student performance. There is also evidence to suggest that students´ approach to learn, i.e. whether they adopt a deep or surface approach influence learning outcome. There is, however, little research ...... the situated motivation provided by the institution, and by understanding the role of these factors, we may improve our ability to create learning environments that provide opportunities for students to experience progress in their learning....... and multivariate hierarchical regression analyses, adjusting for the remaining variables. Results: Both self-efficacy, test-anxiety, and perception of the teaching environment appeared to be strong independent predictors of student approaches to learning even when controlling for other motivational factors....... Conclusion: Although successful learning largely depends on knowledge and skills, factors such as self efficacy and test anxiety play an important role as predictors of students’ learning approaches, and subsequent learning outcomes. Because students are not always internally motivated, they sometimes need...

  8. Blended Learning in Higher Education: Three Different Design Approaches

    Science.gov (United States)

    Alammary, Ali; Sheard, Judy; Carbone, Angela

    2014-01-01

    Blended learning has been growing in popularity as it has proved to be an effective approach for accommodating an increasingly diverse student population whilst adding value to the learning environment through incorporation of online teaching resources. Despite this growing interest, there is ongoing debate about the definition of the concept of…

  9. Learning Objects Update: Review and Critical Approach to Content Aggregation

    Science.gov (United States)

    Balatsoukas, Panos; Morris, Anne; O'Brien, Ann

    2008-01-01

    The structure and composite nature of a learning object is still open to interpretation. Although several theoretical studies advocate integrated approaches to the structure and aggregation level of learning objects, in practice, many content specifications, such as SCORM, IMS Content Packaging, and course authoring tools, do not explicitly state…

  10. A Challenge-Feedback Learning Approach to Teaching International Business

    Science.gov (United States)

    Sternad, Dietmar

    2015-01-01

    This article introduces a challenge-feedback learning (CFL) approach based on the goal-setting theory of human motivation, the deliberate practice theory of expert performance, and findings from the research on active and collaborative learning. The core of the teaching concept is the CFL cycle in which students repeatedly progress through four…

  11. Situated Poetry Learning Using Multimedia Resource Sharing Approach

    Science.gov (United States)

    Yang, Che-Ching; Tseng, Shian-Shyong; Liao, Anthony Y. H.; Liang, Tyne

    2013-01-01

    Educators have emphasized the importance of situating students in an authentic learning environment. By using such approach, teachers can encourage students to learn Chinese poems by browsing content resources and relevant online multimedia resources by using handheld devices. Nevertheless, students in heterogeneous network environments may have…

  12. Towards sustainability: artificial intelligent based approach for soil stabilization using various pozzolans

    KAUST Repository

    Ouf, M. S.

    2012-07-03

    Due to the gradual depletion in the conventional resources, searching for a more rational road construction approach aimed at reducing the dependence on imported materials while improving the quality and durability of the roads is necessary. A previous study carried out on a sample of Egyptian soil aimed at reducing the road construction cost, protect the environment and achieving sustainability. RoadCem, ground granulated blast furnace slag (GGBS), lime and ordinary Portland cement (OPC) were employed to stabilise the Egyptian clayey soil. The results revealed that the unconfined compressive strength (UCS) of the test soil increased while the free swelling percent (FSP) decreased with an increase in the total stabiliser and the curing period. This paper discusses attempts to reach optimum stabilization through: (1) Recognizing the relationship between the UCS/FSP of stabilized soil and the stabilization parameters using artificial neural network (ANN); and (2) Performing a backward optimization on the developed (ANN) model using general algorithm (GA) to meet practical design preferences. © 2012 WIT Press.

  13. New Artificial Immune System Approach Based on Monoclonal Principle for Job Recommendation

    Directory of Open Access Journals (Sweden)

    Shaha Al-Otaibi

    2016-04-01

    Full Text Available Finding the best solution for an optimization problem is a tedious task, specifically in the presence of enormously represented features. When we handle a problem such as job recommendations that have a diversity of their features, we should rely to metaheuristics. For example, the Artificial Immune System which is a novel computational intelligence paradigm achieving diversification and exploration of the search space as well as exploitation of the good solutions were reached in reasonable time. Unfortunately, in problems with diversity nature such job recommendation, it produces a huge number of antibodies that causes a large number of matching processes affect the system efficiency. To leverage this issue, we present a new intelligence algorithm inspired by immunology based on monoclonal antibodies production principle that, up to our knowledge, has never applied in science and engineering problems. The proposed algorithm recommends ranked list of best applicants for a certain job. We discussed the design issues, as well as the immune system processes that should be applied to the problem. Finally, the experiments are conducted that shown an excellence of our approach.

  14. Artificial multiple criticality and phase equilibria: an investigation of the PC-SAFT approach.

    Science.gov (United States)

    Yelash, Leonid; Müller, Marcus; Paul, Wolfgang; Binder, Kurt

    2005-11-01

    The perturbed-chain statistical associating fluid theory (PC-SAFT) is studied for a wide range of temperature, T, pressure, p, and (effective) chain length, m, to establish the generic phase diagram of polymers according to this theory. In addition to the expected gas-liquid coexistence, two additional phase separations are found, termed "gas-gas" equilibrium (at very low densities) and "liquid-liquid" equilibrium (at densities where the system is expected to be solid already). These phase separations imply that in one-component polymer systems three critical points occur, as well as equilibria of three fluid phases at triple points. However, Monte Carlo simulations of the corresponding system yield no trace of the gas-gas and liquid-liquid equilibria, and we conclude that the latter are just artefacts of the PC-SAFT approach. Using PC-SAFT to correlate data for polybutadiene melts, we suggest that discrepancies in modelling the polymer density at ambient temperature and high pressure can be related to the presumably artificial liquid-liquid phase separation at lower temperatures. Thus, particular care is needed in engineering applications of the PC-SAFT theory that aims at predicting properties of macromolecular materials.

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

    International Nuclear Information System (INIS)

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

  16. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Hong-Hai Tran

    2014-01-01

    Full Text Available Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

  17. An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies

    CERN Document Server

    Teimoorinia, Hossen; Ellison, Sara L

    2016-01-01

    We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400,000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy properties for training the pattern recognition algorithm to recognise star forming and passive systems, for a `training set' of $\\sim$100,000 galaxies. We then apply the ANN model to a `verification set' of $\\sim$100,000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and B/T are excellent predictors of the passive state of the system, indicating that properties related to the...

  18. A NOVEL APPROACH FOR SELECTION OF LEARNING OBJECTS FOR PERSONALIZED DELIVERY OF E-LEARNING CONTENT

    Directory of Open Access Journals (Sweden)

    D. Anitha

    2013-02-01

    Full Text Available Personalized E-learning, as an intelligent package of technology enhanced education tends to overrule the traditional practices of static web based E-learning systems. Delivering suitable learning objects according to the learners’ knowledge, preferences and learning styles makes up the personalized E-learning. This paper proposes a novel approach for classifying and selecting learning objects for different learning styles proposed by Felder and Silverman The methodology adheres to the IEEE LOM standard and maps the IEEE LO Metadata to the identified learning styles based on rule based classification of learning objects. A pilot study on the research work is performed and evaluation of the system gives an encouraging result.

  19. Service-Learning Pedagogy: Benefits of a Learning Community Approach

    Science.gov (United States)

    Flinders, Brooke A.

    2013-01-01

    Service-learning is, by nature, continually evolving. Seifer (1996) stressed the importance of partnerships between communities and schools, and stated that reflection should facilitate the connection between practice and theory, and lead to critical thinking. Before these reflective activities occur, however, much can be done to maximize…

  20. Learning and Experience - a Psycho-societal Approach

    DEFF Research Database (Denmark)

    Olesen, Henning Salling

    2016-01-01

    . Learning is theorized as dynamic subjective experience of (socially situated) realities, counting on individual subjectivity as well as subjective aspects of social interaction. This psycho-societal theory of subjective experiences conceptualizes individual psychic development as interactional experience......Abstract: This chapter introduces a psycho-societal approach to theorizing learning, combining a materialist theory of socialization with a hermeneutic interpretation methodology. The term "approach" indicates the intrinsic connection between theory, empirical research process and epistemic subject...

  1. Exploring changes in ocupational therapy students' approaches to learning

    OpenAIRE

    Chapman, J.; Watson, J.; Adams, J.

    2006-01-01

    This article describes a longitudinal cohort study that examined the preferred approaches to learning of pre-registration occupational therapy students (N=55) as they progressed through the three years of an undergraduate BSc (Hons) programme. Students’ orientations to learning were measured using the Short Inventory of Approaches to Studying (ASI) (Entwistle 1981) and results were compared descriptively across repeat measures undertaken during each year of study. Inferential statistics are ...

  2. Sensorless speed estimation of an AC induction motor by using an artificial neural network approach

    Science.gov (United States)

    Alkhoraif, Abdulelah Ali

    Sensorless speed detection of an induction motor is an attractive area for researchers to enhance the reliability of the system and to reduce the cost of the components. This paper presents a simple method of estimating a rotational speed by utilizing an artificial neural network (ANN) that would be fed by a set of stator current frequencies that contain some saliency harmonics. This approach allows operators to detect the speed in induction motors such an approach also provides reliability, low cost, and simplicity. First, the proposed method is based on converting the stator current signals to the frequency domain and then applying a tracking algorithm to the stator current spectrum in order to detect frequency peaks. Secondly, the ANN has to be trained by the detected peaks; the training data must be from very precise data to provide an accurate rotor speed. Moreover, the desired output of the training is the speed, which is measured by a tachometer simultaneously with the stator current signal. The databases were collected at many different speeds from two different types of AC induction motors, wound rotor and squirrel cage. They were trained and tested, so when the difference between the desired speed value and the ANN output value reached the wanted accuracy, the system does not need to use the tachometer anymore. Eventually, the experimental results show that in an optimal ANN design, the speed of the wound rotor induction motor was estimated accurately, where the testing average error was 1 RPM. The proposed method has not succeeded to predict the rotor speed of the squirrel cage induction motor precisely, where the smallest testing­average error that was achieved was 5 RPM.

  3. Medical students’ approaches to learning over a full degree programme

    Directory of Open Access Journals (Sweden)

    William A. Reid

    2012-08-01

    Full Text Available Students take three approaches to learning and studying: deep, surface and strategic, influenced by the learning environment. Following the General Medical Council's report "Tomorrow's Doctors," a deep approach was cultivated in Years 1 and 2 of a university undergraduate medical programme by introducing explicit written learning objectives constructed according to Biggs' SOLO taxonomy, problem-based learning and constructively aligned in-course assignments and examinations. The effect of these changes was measured with the Approaches to Study Skills Inventory for Students (ASSIST. Scores were highest for a deep approach and lowest for a surface approach and showed relatively little change during the degree programme, apart from a slight fall in the scores for a surface approach, particularly for students undertaking an intercalated science degree. Possible explanations include: students' approaches may be established prior to university entry; deep scores were already high at the beginning of the programme and may be difficult to increase further; the changes in learning environment may not be strong enough to alter approaches which students perceive as having been successful.

  4. Neutrosophic Logic Approach for Evaluating Learning Management Systems

    Directory of Open Access Journals (Sweden)

    Nouran Radwan

    2016-08-01

    Full Text Available Uncertainty in expert systems is essential research point in artificial intelligence domain. Uncertain knowledge representation and analysis in expert systems is one of the challenges that takes researchers concern as different uncertainty types which are imprecision, vagueness, ambiguity, and inconsistence need different handling models. This paper reviews some of the multivalued logic models which are fuzzy set, intuitionistic fuzzy set, and suggests a new approach which is neutrosophic set for handling uncertainty in expert systems to derive decisions.

  5. Determining the Approaches of High School Students to Learning Physics

    Directory of Open Access Journals (Sweden)

    Gamze Sezgin Selcuk

    2014-06-01

    Full Text Available The primary objective of this research was to adapt the Approaches to Learning Scale developed for the university level by Ellez & Sezgin (2000 to a high school level physics (Approaches to Learning Physics Scale. The secondary objective was to use the adapted scale to examine the approaches of high school students to learning physics and explore how this variable changes according to gender and level of achievement in physics. The adapted scale was applied to a total of 329 high school students in the province of İzmir, Turkey for the purpose of testing the scale's validity and reliability. The reliability coefficient for the whole of the scale was found to be 0.86. The data related to the secondary objective of the research were analyzed using frequencies, percentages, means, standard deviation, two-way multivariate analysis (two-way MANOVA, and follow-up tests. It was determined from the results of the analysis that the students' preference for both a deep and a surface approach to learning physics was slightly above average. It was found the students did not display a significant difference in their approaches to learning according to the gender variable but that there were significant differences between the students' approaches to learning according to the variable of achievement.

  6. Analysis and simulation of creativity learning by means of artificial neural networks.

    Science.gov (United States)

    Memmert, Daniel; Perl, Jürgen

    2009-04-01

    The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 participants in standardized test situations was tested in a creative training program lasting six months. The results from the DyCoNG-based simulation show that the network is able to separate main process types and reproduce recorded creative learning processes by means of simulation. The results are discussed in connection with practical implications in team sports and with a view to future investigations. PMID:19110331

  7. Automatic speech recognition a deep learning approach

    CERN Document Server

    Yu, Dong

    2015-01-01

    This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.

  8. Social Learning in Bumblebees (Bombus impatiens: Worker Bumblebees Learn to Manipulate and Forage at Artificial Flowers by Observation and Communication within the Colony

    Directory of Open Access Journals (Sweden)

    Hamida B. Mirwan

    2013-01-01

    Full Text Available Social learning occurs when one individual learns from another, mainly conspecific, often by observation, imitation, or communication. Using artificial flowers, we studied social learning by allowing test bumblebees to (a see dead bumblebees arranged in foraging positions or (b watch live bumblebees actually foraging or (c communicate with nestmates within their colony without having seen foraging. Artificial flowers made from 1.5 mL microcentrifuge tubes with closed caps were inserted through the centres of blue 7 cm plastic discs as optical signals through which the bees could not forage. The reinforcer reward syrup was accessible only through holes in the sides of the tubes beneath the blue discs. Two colonies (A and B were used in tandem along with control (C and D colonies. No bee that was not exposed (i.e., from the control colonies (C and D to social learning discovered the access holes. Inside colony B, we imprisoned a group of bees that were prevented from seeing or watching. Bees that saw dead bumblebees in foraging positions, those that watched nest-mates foraging, and those that had only in-hive communication with successful foragers all foraged successfully. The means of in-hive communication are not understood and warrant intense investigation.

  9. An Autoencoder Approach to Learning Bilingual Word Representations

    OpenAIRE

    P, Sarath Chandar A; Lauly, Stanislas; Larochelle, Hugo; Khapra, Mitesh M.; Ravindran, Balaraman; Raykar, Vikas; Saha, Amrita

    2014-01-01

    Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are aligned between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-w...

  10. Learning emergence: adaptive cellular automata façade trained by artificial neural networks

    OpenAIRE

    Skavara, M. M. E.

    2009-01-01

    This thesis looks into the possibilities of controlling the emergent behaviour of Cellular Automata (CA) to achieve specific architectural goals. More explicitly, the objective is to develop a performing, adaptive building facade, which is fed with the history of its achievements and errors, to provide optimum light conditions in buildings’ interiors. To achieve that, an artificial Neural Network (NN) is implemented. However, can an artificial NN cope with the complexity of suc...

  11. On the Future Possibilities of Artificial Intelligence Based M-Learning Content Development

    OpenAIRE

    KÖSE, Utku; TÜFEKÇİ, Aslıhan

    2015-01-01

    Abstract—Artificial Intelligence is widely used in almost every field of the modern life; in order to provide effective solutions for real-world problems. It can be definitely said that this research field has a remarkable power on shaping the future of the humankind. When we take today's technologies into consideration, it is also seen that usage of Artificial Intelligence and mobile applications together is a key element for many future applications. At this point, main objective of this st...

  12. Interrelations among University Students' Approaches to Learning, Regulation of Learning, and Cognitive and Attributional Strategies: A Person Oriented Approach

    Science.gov (United States)

    Heikkila, Annamari; Niemivirta, Markku; Nieminen, Juha; Lonka, Kirsti

    2011-01-01

    This study investigated the relationships among approaches to learning, regulation of learning, cognitive and attributional strategies, stress, exhaustion, and study success. University students (N = 437) from three faculties filled in a questionnaire concerning their self-reported study behaviour, cognitive strategies, and well-being. Their…

  13. Newton's First Law: A Learning Cycle Approach

    Science.gov (United States)

    McCarthy, Deborah

    2005-01-01

    To demonstrate how Newton's first law of motion applies to students' everyday lives, the author developed a learning cycle series of activities on inertia. The discrepant event at the heart of these activities is sure to elicit wide-eyed stares and puzzled looks from students, but also promote critical thinking and help bring an abstract concept…

  14. Dramatic Education, An Interdisciplinary Approach to Learning.

    Science.gov (United States)

    Landy, Robert Jay

    This thesis argues that dramatic education is a subject matter whose content is four interrelated disciplines: theater, language arts, humanistic education, and social-psychology. It is also a process of learning crucial artistic, linguistic, humanistic, and scientific issues through the basic dramatic method of dramatization. The history of…

  15. The Reading Approach & Second Language Learning

    Institute of Scientific and Technical Information of China (English)

    Dilian He Nicholson

    2004-01-01

    @@Learning a second language at the adult stage of life poses a complex situation of interacting already formed first tongue set in reflective mode of development and the new language with its all new:philosophy,syntax,spextra of idioms and implied meanings,humour,metaphors and rhythm.

  16. Teaching and Learning Forgiveness: A Multidimensional Approach

    Science.gov (United States)

    Malcolm, Lois; Ramsey, Janet

    2006-01-01

    This essay seeks to illumine the teaching and learning of the practice of forgiveness by relating a range of theoretical perspectives (theological, psychological, and socio-cultural) to the process of cultivating the practical wisdom needed for forgiveness. We discuss how a Trinitarian "epistemology of the cross" might lead one to a new way of…

  17. Flipped Approach to Mobile Assisted Language Learning

    Science.gov (United States)

    Yamamoto, Junko

    2013-01-01

    There are abundant possibilities for using smart phones and tablet computers for foreign language learning. However, if there is an emphasis on memorization or on technology, language learners may not develop proficiency in their target language. Therefore, language teachers should be familiar with strategies for facilitating creative…

  18. Learning analytics approach of EMMA project

    NARCIS (Netherlands)

    Tammets, Kairit; Brouns, Francis

    2014-01-01

    The EMMA project provides a MOOC platform to aggregate and delivers massive open online courses (MOOC) in multiple languages from a variety of European universities. Learning analytics play an important role in MOOCs to support the individual needs of the learner.

  19. A Belgian Approach to Learning Disabilities.

    Science.gov (United States)

    Hayes, Cheryl W.

    The paper reviews Belgian philosophy toward the education of learning disabled students and cites the differences between American behaviorally-oriented theory and Belgian emphasis on identifying the underlying causes of the disability. Academic methods observed in Belgium (including psychodrama and perceptual motor training) are discussed and are…

  20. A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks.

    Science.gov (United States)

    Yue, Kun; Fang, Qiyu; Wang, Xiaoling; Li, Jin; Liu, Weiyi

    2015-12-01

    Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.

  1. A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Raju Dara

    2014-10-01

    Full Text Available Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

  2. Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii.

    Science.gov (United States)

    Wang, Yao; Basu, Sukanta

    2016-05-15

    In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence (Cn2) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed Cn2 data near the Mauna Loa Observatory, Hawaii are utilized for validation. The proposed approach has demonstrated its prowess by capturing the temporal evolution of Cn2 remarkably well. More interestingly, this ANN approach is found to outperform a widely used similarity theory-based conventional formulation for all the prevalent atmospheric conditions (including strongly stratified conditions). PMID:27176996

  3. A manifold learning approach to target detection in high-resolution hyperspectral imagery

    Science.gov (United States)

    Ziemann, Amanda K.

    Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the

  4. A multi-objective approach to evolving artificial neural networks for coronary heart disease classification

    OpenAIRE

    Shenfield, Alex; Rostami, Shahin

    2015-01-01

    The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and ...

  5. A Socioeconomic Approach to the Development of E-Learning

    Directory of Open Access Journals (Sweden)

    Henrik Johannsen Duus

    2009-07-01

    Full Text Available A multitude of products, systems, approaches, views and notions characterize the field of e-learning. This article attempts to disentangle the field by using economic and sociological theories, theories of marketing management and strategy as well as practical experience gained by the author while working with leading edge suppliers of e-learning. On this basis, a distinction between knowledge creation e-learning and knowledge transfer e-learning is made. The various views are divided into four different ideal-typical paradigms, each with its own characteristics and limitations. Selecting the right paradigm to use in the development of an e-learning strategy may prove crucial to success. Implications for the development of an e-learning strategy in businesses and educational institutions are outlined.

  6. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

    Science.gov (United States)

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

    2013-09-01

    The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds.

  7. Galaxy morphology - an unsupervised machine learning approach

    CERN Document Server

    Schutter, Andrew

    2015-01-01

    Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being ...

  8. Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence.

    Science.gov (United States)

    Aoun, Bachir

    2016-05-01

    A new Reverse Monte Carlo (RMC) package "fullrmc" for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group.

  9. Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence.

    Science.gov (United States)

    Aoun, Bachir

    2016-05-01

    A new Reverse Monte Carlo (RMC) package "fullrmc" for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. PMID:26800289

  10. Computer Mediated Learning: An Example of an Approach.

    Science.gov (United States)

    Arcavi, Abraham; Hadas, Nurit

    2000-01-01

    There are several possible approaches in which dynamic computerized environments play a significant and possibly unique role in supporting innovative learning trajectories in mathematics in general and geometry in particular. Describes an approach based on a problem situation and some experiences using it with students and teachers. (Contains 15…

  11. 3 Colleges' Different Approaches Shape Learning in Econ 101

    Science.gov (United States)

    Berrett, Dan

    2012-01-01

    No matter the college, a class in the principles of microeconomics is likely to cover the discipline's greatest hits. The author attends three economics courses at three colleges, and finds three very different approaches. In this article, the author discusses three colleges' different approaches that shape learning in Econ 101.

  12. Work Transitions as Told: A Narrative Approach to Biographical Learning

    Science.gov (United States)

    Hallqvist, Anders; Hyden, Lars-Christer

    2013-01-01

    In this article, we introduce a narrative approach to biographical learning; that is, an approach that considers autobiographical storytelling as a practice through which claims about life history are performed and negotiated. Using insights from narrative theory, we highlight evaluations in those narratives and suggest their crucial role in…

  13. The scientific learning approach using multimedia-based maze game to improve learning outcomes

    Science.gov (United States)

    Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

    2016-02-01

    The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).

  14. Case-based approaches for knowledge application and organisational learning

    DEFF Research Database (Denmark)

    Wang, Chengbo; Johansen, John; Luxhøj, James T.;

    2005-01-01

    structured processes to execute the organisational learning and knowledge application, which intend to guide the practitioners during the process of manufacturing competence development and improvement. They are based on Case-Based Reasoning (CBR) methodology and rely on cases as the primary knowledge supply....... These practices and activity patterns are based on learning and applying the knowledge internal and external to an organisation. To ensure their smooth formulation process, there are two important techniques designed – an expert adaptation approach and an expert evaluation approach. These two approaches provide...

  15. Approaches to learning and academic performance of Turkish university students

    Directory of Open Access Journals (Sweden)

    Leyla Harputlu *

    2011-12-01

    Full Text Available This paper reports findings with regard to approaches to learning of Turkish students. The term “approaches to learning” refers to the idea that learners perceive and process information in very different ways. The study is set out to (i explore and describe the approaches of learning of university students; (ii explore the relationship between approaches to learning constructs, (iii explore how the learning approaches of Turkish higher education students in combination with gender and academic discipline, year affect and academic performance; Employing a correlational research design- 44-item 1995 version of the RASI and the cumulative grade point, the study was conducted in two departments in two institutions of higher education: one humanities and one engineering. Total 160 students participated. This paper discusses firstly the findings of this study in the light of other research carried out in this area and secondly, and more importantly, in the light of its contribution towards a better understanding of the learning needs of Turkish university students.

  16. Associations between the Classroom Learning Environment and Student Engagement in Learning 1: A Rasch Model Approach

    Science.gov (United States)

    Cavanagh, Rob

    2012-01-01

    This report is about one of two phases in an investigation into associations between student engagement in classroom learning and the classroom learning environment. Both phases applied the same instrumentation to the same sample. The difference between the phases was in the measurement approach applied. This report is about application of the…

  17. A Collaborative Game-Based Learning Approach to Improving Students' Learning Performance in Science Courses

    Science.gov (United States)

    Sung, Han-Yu; Hwang, Gwo-Jen

    2013-01-01

    In this study, a collaborative game-based learning environment is developed by integrating a grid-based Mindtool to facilitate the students to share and organize what they have learned during the game-playing process. To evaluate the effectiveness of the proposed approach, an experiment has been conducted in an elementary school natural science…

  18. A Game-Based Learning Approach to Improving Students' Learning Achievements in a Nutrition Course

    Science.gov (United States)

    Yien, Jui-Mei; Hung, Chun-Ming; Hwang, Gwo-Jen; Lin, Yueh-Chiao

    2011-01-01

    The aim of this study was to explore the influence of applying a game-based learning approach to nutrition education. The quasi-experimental nonequivalent-control group design was adopted in a four-week learning activity. The participants included sixty-six third graders in two classes of an elementary school. One of the classes was assigned to be…

  19. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    Science.gov (United States)

    Ismail, Habsah; Hassan, Aminuddin; Muhamad, Mohd. Mokhtar; Ali, Wan Zah Wan; Konting, Mohd. Majid

    2013-01-01

    This is an investigation of the students' beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items…

  20. Learning from tutorials: a qualitative study of approaches to learning and perceptions of tutorial interaction

    DEFF Research Database (Denmark)

    Herrmann, Kim Jesper

    2014-01-01

    This study examines differences in university students’ approaches to learning when attending tutorials as well as variation in students’ perceptions of tutorials as an educational arena. In-depth qualitative analysis of semi-structured interviews with undergraduates showed how surface and deep...... approaches to learning were revealed in the students’ note-taking, listening, and engaging in dialogue. It was also shown how variation in the students’ approaches to learning were coherent with variation in the students’ perceptions of the tutors’ pedagogical role, the value of peer interaction......, and the overall purpose of tutorials. The results are discussed regarding the paradox that students relying on surface approaches to learning seemingly are the ones least likely to respond to tutorials in the way they were intended....

  1. An Easy and Effective Approach to English Vocabulary Learning

    Institute of Scientific and Technical Information of China (English)

    陈以欣; 陈建章

    2013-01-01

    Vocabulary poses a challenge to most English learners. There are some misconcepts and mismanagements in this area. In view of the problems arising, this article makes particular points addressing the issues concerned. Firstly, it discusses the position of vocabulary in English language and some important notions concerning vocabulary learning. Furthermore, it examines the condition of learnt vocabulary in the mental space of learners. Finally, it proposes some useful strategies and approaches applicable to vocabulary learning process.

  2. SOCIAL ENTREPRENEURSHIP: A GROUNDED LEARNING APPROACH TO SOCIAL VALUE CREATION

    OpenAIRE

    BRETT R. SMITH; TERRI FELDMAN BARR; SAULO D. BARBOSA; JILL R. KICKUL

    2008-01-01

    The value of the inclusion of social entrepreneurship in entrepreneurship education courses and programs is considered in light of the increase in social entrepreneurial ventures worldwide as well as changing business school requirements. Using a grounded learning theory approach as a foundation, we consider factors unique to social entrepreneurship and present a live case social venture which provides hands-on experience to students. Student comments regarding their learning through this exp...

  3. Learning Theory Approach to Minimum Error Entropy Criterion

    OpenAIRE

    Hu, Ting; Fan, Jun; Wu, Qiang; Zhou, Ding-Xuan

    2012-01-01

    We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for this MEE algorithm and explicit error bounds are provided in terms of the approximation ability and capacity of the involved hypothesis space when the MEE scaling parameter is large. Novel asymptotic analysis is conducted for the generalization error associated with Renyi's entropy and a Parzen window function, to over...

  4. Stochastic learning in co-ordination games : a simulation approach

    OpenAIRE

    Zaninotto, Enrico; Rossi, Alessandro; Gaio, Loris

    1999-01-01

    In the presence of externalities, consumption behaviour depends on the solution of a co-ordination problem. In our paper we suggest a learning approach to the study of co-ordination in consumption contexts where agents adjust their choices on the basis of the reinforcement (payoff) they receive during the game. The results of simulations allowed us to distinguish the roles of different aspects of learning in enabling co-ordination within a population of agents. Our main results highlight: 1. ...

  5. Factors Influencing Sensitivity to Lexical Tone in an Artificial Language: Implications for Second Language Learning

    Science.gov (United States)

    Caldwell-Harris, Catherine L.; Lancaster, Alia; Ladd, D. Robert; Dediu, Dan; Christiansen, Morten H.

    2015-01-01

    This study examined whether musical training, ethnicity, and experience with a natural tone language influenced sensitivity to tone while listening to an artificial tone language. The language was designed with three tones, modeled after level-tone African languages. Participants listened to a 15-min random concatenation of six 3-syllable words.…

  6. Designing e-learning solutions with a client centred approach

    DEFF Research Database (Denmark)

    Ørngreen, Rikke; Nielsen, Janni; Levinsen, Karin

    2008-01-01

      This paper claims that the strategies applied in designing e-learning solutions tend to focus on how to proceed after the precondition, e.g., learners requirements, pedagogical choice, etc., have been decided upon. Investigating the HCI research field, we find that the methodological approaches...... as the organisation that has initiated the e-learning project and needs to manage the e-learning system after its development. Through the Client Centred Design and in close collaboration with the client, three strategic issues are uncovered and strategic models are presented for each. These models are complementary...

  7. A Communicative Approach to College English Grammar Teaching and Learning

    Institute of Scientific and Technical Information of China (English)

    LI Yong-xian

    2016-01-01

    In response to the misconception that Communicative Language Teaching means no teaching of grammar, it is argued that grammar is as important as traffic rules for safe and smooth traffic on the road. To achieve appropriate and effective commu-nication, a communicative approach to college grammar teaching and learning is proposed. Both teachers and learners should change their attitudes toward and conceptions about grammar teaching and learning;additionally, teaching grammar in the com-pany of reading and writing helps learners learn and acquire grammar in meaningful contexts.

  8. A Suggested approach to teaching and learning grammar

    Institute of Scientific and Technical Information of China (English)

    Yang Jijun; Niu Conglin

    2008-01-01

    The teaching and learning of grammar used to be processed at the sentence level with paying litttle attention to discourse and context.Students Were familiar with alot of gammatical terms and were able to remember many rules .However,they often wondered why they could not use English properly and fluently and fluently although they had learned the grammatical rules quite well.This essay,taking grammar as resource for communication,suggests an approach by using ideational frameworks to the teacxhing and learning of grammar in a communicative context.

  9. Learning from events: a process approach

    NARCIS (Netherlands)

    Drupsteen, L.; Zwetsloot, G.; Groeneweg, J.

    2012-01-01

    Many organizations try to prevent reoccurrence of incidents by analyzing incidents and implement recommendations based in their findings. Unfortunately this approach is not without pitfalls. Between 'reporting' and 'evaluation of the effect of actions' there are several hurdles to be taken. Only by

  10. Methodological approaches to learning self-education

    OpenAIRE

    BORANBAYEVA AKTOLKYN; BERKIMBAYEV KAMALBEK; ARYMBAYEVA KULIMKHAN

    2016-01-01

    In article reveals the concepts «self-education» and components of self-education, revealed scientific and methodological approaches to education and self-education. Defined functions of personally focused education and self-education activity of future teacher.

  11. A Bayesian Sampling Approach to Exploration in Reinforcement Learning

    CERN Document Server

    Asmuth, John; Littman, Michael L; Nouri, Ali; Wingate, David

    2012-01-01

    We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to resample and how to combine the models. We show that our algorithm achieves nearoptimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.

  12. A Variance Based Active Learning Approach for Named Entity Recognition

    Science.gov (United States)

    Hassanzadeh, Hamed; Keyvanpour, Mohammadreza

    The cost of manually annotating corpora is one of the significant issues in many text based tasks such as text mining, semantic annotation and generally information extraction. Active Learning is an approach that deals with reduction of labeling costs. In this paper we proposed an effective active learning approach based on minimal variance that reduces manual annotation cost by using a small number of manually labeled examples. In our approach we use a confidence measure based on the model's variance that reaches a considerable accuracy for annotating entities. Conditional Random Field (CRF) is chosen as the underlying learning model due to its promising performance in many sequence labeling tasks. The experiments show that the proposed method needs considerably fewer manual labeled samples to produce a desirable result.

  13. Forward-oriented design for learning: illustrating the approach

    Directory of Open Access Journals (Sweden)

    Yannis Dimitriadis

    2013-08-01

    Full Text Available This paper concerns sustainable approaches to design for learning, emphasising the need for designs to be able to thrive outside of the protective niches of project-based innovation. It builds on the “in medias res” framework and more specifically on a forward-oriented approach to design for learning: one that takes a pro-active design stance with respect to each of the phases of an extended lifecycle. We draw on fieldwork notes and interview data to describe two cases that illustrate some of the key features of the approach. Recommendations for further R&D in the area of design for learning are provided, derived from the theoretical framework, and illustrated in this paper.

  14. The Effect of 5E Learning Model on Seventh Grade Students’ Approaches to Learning

    OpenAIRE

    Eylem Yıldız Feyzioğlu; Ömer Ergin

    2012-01-01

    The purpose of this study is to investigate the effect of 5E learning model on three seventh grade students’ approaches to learning. The students’ approaches to “remembering what s/he learned”, “the goal of learning” and “encountering the difficulty in learning” before and after the application of 5E model is presented. Data was gathered through these students’ scores on the scale of “Deep Approaches to Learning” and “Surface Approaches to Learning”. Pre and post interviews were also conducte...

  15. Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM

    Directory of Open Access Journals (Sweden)

    Arash Ebrahimabadi

    2015-10-01

    Full Text Available Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, roadheaders are mechanical miners which have been extensively used in tunneling, mining and civil industries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs of medium-duty roadheaders by using artificial neural network (ANN approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP and Kohonen self-organizing feature map (KSOFM are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders' performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD, intact rock properties such as uniaxial compressive strength (UCS and Brazilian tensile strength (BTS, and one output parameter (ICR. In order to have more validation on MLP outputs, KSOFM visualization was applied. The mean square error (MSE and regression coefficient (R of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 × 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks

  16. Prediction of roadheaders’ performance using artificial neural network approaches (MLP and KOSFM)

    Institute of Scientific and Technical Information of China (English)

    Arash Ebrahimabadi; Mohammad Azimipour; Ali Bahreini

    2015-01-01

    Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, road-headers are mechanical miners which have been extensively used in tunneling, mining and civil in-dustries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs)) of medium-duty roadheaders by using artificial neural network (ANN) approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM) are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders’ performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD), intact rock properties such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS), and one output parameter (ICR). In order to have more validation on MLP outputs, KSOFM visu-alization was applied. The mean square error (MSE) and regression coefficient (R) of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 ? 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks have a

  17. When to approach novel prey cues? Social learning strategies in frog-eating bats.

    Science.gov (United States)

    Jones, Patricia L; Ryan, Michael J; Flores, Victoria; Page, Rachel A

    2013-12-01

    Animals can use different sources of information when making decisions. Foraging animals often have access to both self-acquired and socially acquired information about prey. The fringe-lipped bat, Trachops cirrhosus, hunts frogs by approaching the calls that frogs produce to attract mates. We examined how the reliability of self-acquired prey cues affects social learning of novel prey cues. We trained bats to associate an artificial acoustic cue (mobile phone ringtone) with food rewards. Bats were assigned to treatments in which the trained cue was either an unreliable indicator of reward (rewarded 50% of the presentations) or a reliable indicator (rewarded 100% of the presentations), and they were exposed to a conspecific tutor foraging on a reliable (rewarded 100%) novel cue or to the novel cue with no tutor. Bats whose trained cue was unreliable and who had a tutor were significantly more likely to preferentially approach the novel cue when compared with bats whose trained cue was reliable, and to bats that had no tutor. Reliability of self-acquired prey cues therefore affects social learning of novel prey cues by frog-eating bats. Examining when animals use social information to learn about novel prey is key to understanding the social transmission of foraging innovations.

  18. Scientific approaches and techniques for negotiation : a game theoretic and artificial intelligence perspective

    NARCIS (Netherlands)

    Gerding, E.H.; Bragt, D.D.B. van; La Poutré, J.A.

    2000-01-01

    Due to the rapid growth of electronic environments (such as the Internet) much research is currently being performed on autonomous trading mechanisms. This report contains an overview of the current literature on negotiations in the fields of game theory and artificial intelligence (AI). Game theori

  19. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  20. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  1. New approaches in buffalo artificial insemination programs with special reference to India.

    Science.gov (United States)

    Singh, Inderjeet; Balhara, A K

    2016-07-01

    Buffalo farming has made remarkable progress in productivity mainly because of controlled breeding with artificial insemination (AI) that has proved its worth in breed improvement and breeding managements across the livestock species. Artificial insemination is practiced very little in Europe and East Asian countries with coverage of only 5% buffaloes in Italy, 3.7% in Azerbaijan, 0.3% in Egypt, and 0.1% in Romania although in Bulgaria, 80% buffaloes in large cooperative state farms are subjected to AI. In Turkey, it began in 2002 near Hatay with Italian semen provided by the Food and Agriculture Organization (FAO) Network project. In India, where buffaloes are the most valuable livestock species, research on buffalo specific artificial breeding technologies and adoption of AI by buffalo owners are widely acknowledged. Resultantly, average milk yield of buffaloes in India increased from 3.4 kg in 1992 to 93 to 4.57 kg/day/buffalo in 2009 to 10. In the new millennium, mega projects such as the National Project for Cattle and Buffalo Breeding and the National Dairy Plan were initiated with focus on genetic upgradation of bovine and buffalo population through streamlining AI services and support system in the country. Artificial insemination started in India in the year 1939, and the frozen semen was introduced during late 1960s. During the year 2010 to 11, India produced 63 million bovine frozen semen straws including over one million buffalo semen straws through 49 semen stations. Artificial insemination services are provided through 71,341 AI stations clocking 52 million inseminations with overall conception rate of 35% in bovine and buffalo population. Research is being conducted for improved AI conception rates with synchronization programs and improved frozen-thawed semen quality, and success rates are at par with AI in cattle. PMID:27155729

  2. New approaches in buffalo artificial insemination programs with special reference to India.

    Science.gov (United States)

    Singh, Inderjeet; Balhara, A K

    2016-07-01

    Buffalo farming has made remarkable progress in productivity mainly because of controlled breeding with artificial insemination (AI) that has proved its worth in breed improvement and breeding managements across the livestock species. Artificial insemination is practiced very little in Europe and East Asian countries with coverage of only 5% buffaloes in Italy, 3.7% in Azerbaijan, 0.3% in Egypt, and 0.1% in Romania although in Bulgaria, 80% buffaloes in large cooperative state farms are subjected to AI. In Turkey, it began in 2002 near Hatay with Italian semen provided by the Food and Agriculture Organization (FAO) Network project. In India, where buffaloes are the most valuable livestock species, research on buffalo specific artificial breeding technologies and adoption of AI by buffalo owners are widely acknowledged. Resultantly, average milk yield of buffaloes in India increased from 3.4 kg in 1992 to 93 to 4.57 kg/day/buffalo in 2009 to 10. In the new millennium, mega projects such as the National Project for Cattle and Buffalo Breeding and the National Dairy Plan were initiated with focus on genetic upgradation of bovine and buffalo population through streamlining AI services and support system in the country. Artificial insemination started in India in the year 1939, and the frozen semen was introduced during late 1960s. During the year 2010 to 11, India produced 63 million bovine frozen semen straws including over one million buffalo semen straws through 49 semen stations. Artificial insemination services are provided through 71,341 AI stations clocking 52 million inseminations with overall conception rate of 35% in bovine and buffalo population. Research is being conducted for improved AI conception rates with synchronization programs and improved frozen-thawed semen quality, and success rates are at par with AI in cattle.

  3. Incidental Vocabulary Learning: A Semantic Field Approach

    Directory of Open Access Journals (Sweden)

    Parvaneh Khosravizadeh

    2011-10-01

    Full Text Available

    This study is an attempt to explore the difference between acquiring new words with different semantic fields to which they belong. In other words, the purpose of this study is to scrutinize the contribution of semantic field theory in learning new vocabulary items in an EFL setting. Thirty-eight students of three different levels of education took part in this research. They were exposed to some new words from four different semantic fields, and then they were tested on their acquisition of the words meaning. This exposure was through reading texts and the aim of reading was just comprehension, therefore the words were acquired incidentally. The outcome showed significant differences between groups with different levels of education regarding retention of words from different semantic fields.

  4. A Wittgenstein Approach to the Learning of OO-modeling

    Science.gov (United States)

    Holmboe, Christian

    2004-12-01

    The paper uses Ludwig Wittgenstein's theories about the relationship between thought, language, and objects of the world to explore the assumption that OO-thinking resembles natural thinking. The paper imports from research in linguistic philosophy to computer science education research. I show how UML class diagrams (i.e., an artificial context-free language) correspond to the logically perfect languages described in Tractatus Logico-Philosophicus. In Philosophical Investigations Wittgenstein disputes his previous theories by showing that natural languages are not constructed by rules of mathematical logic, but are language games where the meaning of a word is constructed through its use in social contexts. Contradicting the claim that OO-thinking is easy to learn because of its similarity to natural thinking, I claim that OO-thinking is difficult to learn because of its differences from natural thinking. The nature of these differences is not currently well known or appreciated. I suggest how explicit attention to the nature and implications of different language games may improve the teaching and learning of OO-modeling as well as programming.

  5. Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning

    OpenAIRE

    ten Cate, Carel; Okanoya, Kazuo

    2012-01-01

    The domain of syntax is seen as the core of the language faculty and as the most critical difference between animal vocalizations and language. We review evidence from spontaneously produced vocalizations as well as from perceptual experiments using artificial grammars to analyse animal syntactic abilities, i.e. abilities to produce and perceive patterns following abstract rules. Animal vocalizations consist of vocal units (elements) that are combined in a species-specific way to create highe...

  6. Introduce Lessons Learn Approach as A Phase in SDLC

    Directory of Open Access Journals (Sweden)

    Radhika D Amlani

    2013-01-01

    Full Text Available To understand what Lessons Learned is we must have to understand what is Lessons? Lessons can be derived from any activity. They are a product of operations, exercises, training, experiments, and day-to-day staff work. During the course of our activities most of us will recognize ways of doing things more easily or efficiently that can be passed on to our colleagues and successors to help them avoid problems and do even better than we did before. The term Lessons Learned is broadly used to describe act of learning from experience to achieve improvements. It is an activity used by people to gain benefit in current activity form past activity experience. The idea of lessons learned in an organization is that through a formal approach to learning, individual and the organization can reduce the risk of repeating mistakes and it would increase the chance of success. This means lessons learned approach will reduce failure risk, and improve operational effectiveness and also increase cost efficiency.

  7. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Tian Yonghong

    2010-01-01

    Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  8. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Ling-Yu Duan

    2010-01-01

    Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  9. A developmental approach to learning causal models for cyber security

    Science.gov (United States)

    Mugan, Jonathan

    2013-05-01

    To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

  10. An approach towards problem-based learning in virtual space

    OpenAIRE

    Freudenberg, Lutz S.; Bockisch, Andreas; Beyer, Thomas

    2010-01-01

    Problem-based learning (PBL) is an established and efficient approach to sustainable teaching. Here, we describe translation of PBL into the virtual classroom thereby offering novel teaching aspects in the field of Nuclear Medicine. Our teaching approach is implemented on a "moodle" platform and consists of 2 modules: complementary seminar teaching materials and a virtual PBL-classroom, which can be attended via Skype. Over the course of 4 semesters 539 students have accessed our teaching pla...

  11. Learning about knowledge: A complex network approach

    CERN Document Server

    Costa, L F

    2006-01-01

    This article describes an approach to modeling of knowledge acquisition in terms of complex networks and walks. Each subset of knowledge is represented as a node, and relationship between such knowledge are represented as edges. Two types of edges are considered, corresponding to logical equivalence and implication. Multiple conditional implications are also considered, implying that a node can only be reached after visiting previously a set of nodes (the conditions). It is shown that hierarchical networks, involving a series of interconnected layers containing a connected subnetwork, provides a simple and natural means for avoiding deadlocks, i.e. unreachable nodes. The process of knowledge acquisition can then be simulated by considering a single agent moving along the nodes and edges, starting from the lowest layer. Several configurations of such hierarchical knowledge networks are simulated and the performance of the agent quantified in terms of the percentage of visited nodes after each movement. The Bar...

  12. Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

    Science.gov (United States)

    Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

    2015-01-01

    The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

  13. E-learning paradigms and applications agent-based approach

    CERN Document Server

    Jain, Lakhmi

    2014-01-01

    Teaching and learning paradigms have attracted increased attention especially in the last decade. Immense developments of different ICT technologies and services have paved the way for alternative but effective approaches in educational processes. Many concepts of the agent technology, such as intelligence, autonomy, and cooperation, have had a direct positive impact on many of the requests imposed on modern e-learning systems and educational processes. This book presents the state-of-the-art of e-learning and tutoring systems, and discusses their capabilities and benefits that stem from integrating software agents. We hope that the presented work will be of a great use to our colleagues and researchers interested in the e-learning and agent technology.    

  14. Environmental Factors and Students’ Learning Approaches: A Survey on Malaysian Polytechnics Students

    OpenAIRE

    Ramlee Mustapha; Seri Bunian Mokhtar; Saemah Rahman; Mohd Yusof Husain; Rahayu Ahamad Bahtiar

    2014-01-01

    Several studies have shown the impact of environmental factors on student learning approaches. Despite the importance of such studies, studies on technical learners are few. Thus, this study aimed to determine the influence of learning environment on Polytechnics students’ learning approaches in Malaysia. Learning environment plays an important role in the cognitive, effective and social domains of students because it could improve students’ learning outcomes.  Learning approaches refer to th...

  15. Considering a resource-light approach to learning verb valencies

    CERN Document Server

    Rudnick, Alex

    2012-01-01

    Here we describe work on learning the subcategories of verbs in a morphologically rich language using only minimal linguistic resources. Our goal is to learn verb subcategorizations for Quechua, an under-resourced morphologically rich language, from an unannotated corpus. We compare results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same text in treebank form. The original plan was to use only a morphological analyzer and an unannotated corpus, but experiments suggest that this approach by itself will not be effective for learning the combinatorial potential of Arabic verbs in general. The lower bound on resources for acquiring this information is somewhat higher, apparently requiring a a part-of-speech tagger and chunker for most languages, and a morphological disambiguater for Arabic.

  16. A Web Mining Approach for Personalized E-Learning System

    Directory of Open Access Journals (Sweden)

    Manasi Chakurkar

    2014-01-01

    Full Text Available The Web Mining plays a very important role for the E-learning systems. In personalized E-Learning system, user customize the learning environment based on personal choices. In a general search process ,a hyperlink which is having maximum number of hits will get displayed first . For making a personalized system history of every user need to be saved in the form of user logs. In this paper we present a architecture with the use of Web mining for Web personalization. The proposed system provides a new approach with combination of web usage mining, HITS algorithm and web content mining. It combines hits results on user logs and web page contents with a clustering algorithm called as Lingo clustering algorithm. This proposed system with combined approach gives a better performance than a usage based system. Further the results are computed according to matrices computed from previous and proposed method.

  17. The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes

    Science.gov (United States)

    Nelson Laird, Thomas F.; Shoup, Rick; Kuh, George D.; Schwarz, Michael J.

    2008-01-01

    "Deep learning" represents student engagement in approaches to learning that emphasize integration, synthesis, and reflection. Because learning is a shared responsibility between students and faculty, it is important to determine whether faculty members emphasize deep approaches to learning and to assess how much students employ these approaches.…

  18. The influence of social structure on the propagation of social information in artificial primate groups: a graph-based simulation approach.

    Science.gov (United States)

    Voelkl, Bernhard; Noë, Ronald

    2008-05-01

    Observations of primate groups have shown that social learning can lead to the development of temporal stable traditions or even proto-culture. The social structure of primate groups is highly diverse and it has been proposed that differences in the group structure shall influence the patterns of social information transmission. While empirical studies have mainly focused on the psychological mechanisms of social learning in individuals, the phenomenon of information propagation within the group has received relatively little attention. This might be due to the fact that formal theories that allow actual testing have not been formulated, or were kept too simple, ignoring the social dynamics of multi-agent societies. We want to propose a network approach to social information transmission that (1) preserves the complexity of the social structure of primate groups and (2) allows direct application to empirical data. Results from simulation experiments with artificial group structures confirm that association patterns of group-members influence the expected speed of information transmission during the propagation process. Introducing a forgetting rate shows that under certain conditions the proportion of informed individuals will reach a stable rate in some systems while it will drop to zero in others. This suggests that the likelihood to observe temporal stable traditions shall differ between social systems with different structure.

  19. New approach to the ecotoxicological risk assessment of artificial outdoor sporting grounds.

    Science.gov (United States)

    Krüger, O; Kalbe, U; Richter, E; Egeler, P; Römbke, J; Berger, W

    2013-04-01

    Artificial surfaces for outdoor sporting grounds may pose environmental and health hazards that are difficult to assess due to their complex chemical composition. Ecotoxicity tests can indicate general hazardous impacts. We conducted growth inhibition (Pseudokirchneriella subcapitata) and acute toxicity tests (Daphnia magna) with leachates obtained from batch tests of granular infill material and column tests of complete sporting ground assemblies. Ethylene propylene diene monomer rubber (EPDM) leachate showed the highest effect on Daphnia magna (EC(50) scrap tires made of styrene butadiene rubber (SBR) had the highest effect on P. subcapitata (EC(10) = 4.2% leachate; EC(50) = 15.6% leachate). We found no correlations between ecotoxicity potential of leachates and zinc and PAH concentrations. Leachates obtained from column tests revealed lower ecotoxicological potential. Leachates of column tests of complete assemblies may be used for a reliable risk assessment of artificial sporting grounds. PMID:23337354

  20. Does Instructional Approach Matter? How Elaboration Plays a Crucial Role in Multimedia Learning

    Science.gov (United States)

    Eysink, Tessa H. S.; de Jong, Ton

    2012-01-01

    This study compared the affordances of 4 multimedia learning environments for specific learning processes. The environments covered the same domain but used different instructional approaches: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. Although they all promote an active…

  1. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    Directory of Open Access Journals (Sweden)

    Habsah Ismail

    2013-01-01

    Full Text Available This is an investigation of the students’ beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items measuring students’ epistemological beliefs and 20 items on their learning approaches. Items on epistemological belief were adapted and modified from Schommer’s Epistemological Questionnaire (1990 and Schraw, Bendixen and Dunkle (2000 Epistemic Beliefs Inventory that assesses students' beliefs about simple knowledge, certain knowledge, quick learning, and fixed ability to learn. Items on learning approaches were adapted from Bigg’s forty-two-item Study Process Questionnaire (SPQ, designed for tertiary-level students. The instrument was administered to 1405 students of higher institutions of learning both public and private. Differences in epistemological beliefs among students of these higher institutions, ethnic and between genders were examined.

  2. Re"modeling" College Algebra: An Active Learning Approach

    Science.gov (United States)

    Pinzon, D.; Pinzon, K.; Stackpole, M.

    2016-01-01

    In this paper, we discuss active learning in College Algebra at Georgia Gwinnett College. This approach has been used in more than 20 sections of College Algebra taught by the authors in the past four semesters. Students work in small, structured groups on guided inquiry activities after watching 15-20 minutes of videos before class. We discuss a…

  3. E-learning for Newcomers on the IAEA Milestones Approach

    International Nuclear Information System (INIS)

    Background to E-learning modules: • Member States requesting assistance in introducing nuclear power programs; • Implement training for a broad audience at an overview level; • Foundation to better understand the IAEA Milestones approach; • MS may have problems providing satisfactory (nuclear) Education and Training; • Funded by Republic of Korea

  4. Students’ approaches to learning from other students’ oral presentations

    OpenAIRE

    Grape, Sophie; Jacobsson Svärd, Staffan; Jansson, Peter; Österlund, Michael

    2013-01-01

    A phenomenographic study has been performed in order to investigate students’ approaches to learning from other students’ oral presentations in the context of a compulsory seminar on nuclear accidents in the third year of the nuclear engineering programme at Uppsala University.

  5. Cognitive Ability, Learning Approaches and Personality Correlates of General Knowledge

    Science.gov (United States)

    Furnham, Adrian; Swami, Viren; Arteche, Adriane; Chamorro-Premuzic, Tomas

    2008-01-01

    The relationship between general knowledge (GK) and cognitive ability (IQ and abstract reasoning), learning approaches, and personality ("big five" traits and typical intellectual engagement) was investigated in a sample of 101 British undergraduates. As predicted, GK was positively correlated with cognitive ability (more so with IQ than with…

  6. The Law Review Approach: What the Humanities Can Learn

    Science.gov (United States)

    Mendenhall, Allen

    2013-01-01

    Readers of this journal probably know how the peer review process works in the humanities disciplines and at various journals. Therefore the author explains how the law review process generally works and then what the humanities can learn and borrow from the law review process. He ends by advocating for a hybrid law review/peer review approach to…

  7. Students Approach to Learning and Their Use of Lecture Capture

    Science.gov (United States)

    Vajoczki, Susan; Watt, Susan; Marquis, Nick; Liao, Rose; Vine, Michelle

    2011-01-01

    This study examined lecture capture as a way of enhancing university education, and explored how students with different learning approaches used lecture capturing (i.e., podcasts and vodcasts). Results indicate that both deep and surface learners report increased course satisfaction and better retention of knowledge in courses with traditional…

  8. Blended University Teaching Using Virtual Learning Environments: Conceptions and Approaches

    Science.gov (United States)

    Lameras, Petros; Levy, Philippa; Paraskakis, Iraklis; Webber, Sheila

    2012-01-01

    This paper reports findings from a phenomenographic investigation into blended university teaching using virtual learning environments (VLEs). Interviews with 25 Computer Science teachers in Greek universities illuminated a spectrum of teachers' conceptions and approaches from "teacher-focused and content-oriented", through "student-focused and…

  9. Human Resource Building--An Approach to Service Learning

    Science.gov (United States)

    Rajan, Sonika

    2009-01-01

    Background: Isabella Thoburn College at Lucknow, Uttar Pradesh, India has initiated Service Learning Program for its students through 4 issue based centers. One of the centers AIDS Awareness Center for Counseling, Education, and Training (AACCET) is in the field of HIV/AIDS. It follows 6 pronged approach to achieve its objectives and one of the…

  10. Learning Strategies and Study Approaches of Postsecondary Students with Dyslexia

    Science.gov (United States)

    Kirby, John R.; Silvestri, Robert; Allingham, Beth H.; Parrila, Rauno; La Fave, Chantal B.

    2008-01-01

    The present study describes the self-reported learning strategies and study approaches of college and university students with and without dyslexia and examines the relationship of those characteristics with reading ability. Students with (n = 36) and without (n = 66) dyslexia completed tests measuring reading rate, reading comprehension, reading…

  11. Enhancing the Scholarship of Teaching and Learning: An Organic Approach

    Science.gov (United States)

    Adcroft, Andy; Lockwood, Andrew

    2010-01-01

    The aim of this paper is to report on an experiment in the School of Management at the University of Surrey whereby the Scholarship of Teaching and Learning is being promoted through an approach which is organic in nature. The paper argues that the nature of such scholarship means that its promotion is more likely to be successful when the…

  12. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    Science.gov (United States)

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  13. Can Virtual Museums Motivate Students? Toward a Constructivist Learning Approach

    Science.gov (United States)

    Katz, James E.; Halpern, Daniel

    2015-01-01

    This study aims to assess the effectiveness of immersive environments that have been implemented by museums to attract new visitors. Based on the frameworks introduced by telepresence and media richness theories, and following a constructivist-based learning approach, we argue that the greater the similarity of an online museum experience is to…

  14. Understanding the Science-Learning Environment: A Genetically Sensitive Approach

    Science.gov (United States)

    Haworth, Claire M. A.; Davis, Oliver S. P.; Hanscombe, Ken B.; Kovas, Yulia; Dale, Philip S.; Plomin, Robert

    2013-01-01

    Previous studies have shown that environmental influences on school science performance increase in importance from primary to secondary school. Here we assess for the first time the relationship between the science-learning environment and science performance using a genetically sensitive approach to investigate the aetiology of this link. 3000…

  15. Instructor support for new learning approaches involving technology

    NARCIS (Netherlands)

    Bianco, Manuela; Collis, Betty; Cooke, Andy; Margaryan, Anoush

    2002-01-01

    New learning approaches involving technology are occurring in both universities and company training settings. Critical factors in regard to these changes are the professionals in an organisation responsible for course design, development, and delivery: the instructors and those who support them. In

  16. Approaches to Learning Information Literacy: A Phenomenographic Study

    Science.gov (United States)

    Diehm, Rae-Anne; Lupton, Mandy

    2012-01-01

    This paper reports on an empirical study that explores the ways students approach learning to find and use information. Based on interviews with 15 education students in an Australian university, this study uses phenomenography as its methodological and theoretical basis. The study reveals that students use three main strategies for learning…

  17. Using an Active-Learning Approach to Teach Epigenetics

    Science.gov (United States)

    Colon-Berlingeri, Migdalisel

    2010-01-01

    Epigenetics involves heritable changes in gene expression that do not involve alterations in the DNA sequence. I developed an active-learning approach to convey this topic to students in a college genetics course. I posted a brief summary of the topic before class to stimulate exchange in cooperative groups. During class, we discussed the…

  18. Proposal for an Approach to Artificial Consciousness Based on Self-Consciousness

    OpenAIRE

    Menant, Christophe

    2007-01-01

    Current research on artificial consciousness is focused on phenomenal consciousness and on functional consciousness. We propose to shift the focus to self-consciousness in order to open new areas of investigation. We use an existing scenario where self-consciousness is considered as the result of an evolution of representations. Application of the scenario to the possible build up of a conscious robot also introduces questions relative to emotions in robots. Areas of investigation...

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

    OpenAIRE

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

    2009-01-01

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

  20. A Power Market Forward Curve with Hydrology Dependence - An Approach based on Artificial Neural Networks

    OpenAIRE

    Green, Rikard

    2014-01-01

    This paper develops an hourly forward curve for power markets where the intra-day and intra-week shapes (profiles) depend on the level of the hydrological balance. The shaping model is based on a feed-forward Artificial Neural Network (ANN), which is trained on a historical data set of hourly electricity spot prices from the Nord Pool market and weekly measurements of the Nordic hydrological balance. The yearly seasonal cycle is estimated with historical electricity forward prices from the...

  1. Safety core parameters prediction in research reactors using artificial neural networks: A comparative study of various learning algorithms

    International Nuclear Information System (INIS)

    In recent years, Artificial Neural Networks (ANNs) were applied successfully as an advanced and promising tool for simulating several reactor physics parameters in nuclear engineering applications. The main objective in using such Artificial Intelligent (AI) methods, in the field of nuclear engineering, is to develop simple and 1st estimate models capable of simulating adequately, with reasonable error, important reactor physics parameters in relatively short time comparatively to time consuming and cumbersome reactor physics computer codes. The feasibility of this application has been demonstrated through a previous work done for a typical benchmark 10 Mw IAEA LEU (Low Enriched Uranium) core research reactor, using an adaptive learning rate procedure in a typical back-propagation algorithm in the training process. However, even tough the predictive results achieved are within ±0.7% for Keff and within ±8.5% for Pmax, the convergence time spent during the training phase were of about 36 and 24 hours, respectively for both cited parameters, on a small computational system (300 Mhz Pentium II PC). Hence, this paper suggests one of the suitable ways explored to speed up the training process and to improve neural networks performances by carrying out a comprehensive sensitivity studies on an iterative and multistage calculation process using Neural Network MATLAB Toolbox

  2. Learning about knowledge: A complex network approach

    International Nuclear Information System (INIS)

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges

  3. TRANSLATOR TRAINING BY DISTANCE LEARNING — A DUAL APPROACH1

    Directory of Open Access Journals (Sweden)

    Karnedi Karnedi

    2015-07-01

    Full Text Available A variety of approaches have been adopted by institutions of higher education offering programmes in translator training. Some of these approaches are centred on early training; while others on socio-constructivism. Presenting a facet of training that differs from those generally used in most programmes, this paper examines how task-based approaches used over the course of the curriculum and the project-based approaches adopted in the final year in the form of translation portfolio can be an integral part of an undergraduate translation programme run by distance learning. Translation students’ performance while completing the project online is used as the data supported with online questionnaires. A critical analysis of these two approaches engenders a crucial discussion of increased student autonomy. The project-based translation portfolio is better suited to more advanced students, whereas task-based translation activities are for students at the early stages of training. Nevertheless, the two approaches are compatible and complementary.

  4. A Reinforcement Learning Algorithm Using Multi-Layer Artificial Neural Networks for Semi-Markov Decision Problems

    Directory of Open Access Journals (Sweden)

    Mustafa Ahmet Beyazıt Ocaktan

    2013-06-01

    Full Text Available Real life problems are generally large-scale and difficult to model. Therefore, these problems can't be mostly solved by classical optimisation methods. This paper presents a reinforcement learning algorithm using a multi-layer artificial neural network to find an approximate solution for large-scale semi Markov decision problems. Performance of the developed algorithm is measured and compared to the classical reinforcement algorithm on a small-scale numerical example. According to results of numerical examples, a number of hidden layer are the key success factors, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.

  5. Learning nursing through simulation: A case study approach towards an expansive model of learning.

    Science.gov (United States)

    Berragan, Liz

    2014-08-01

    This study explores the impact of simulation upon learning for undergraduate nursing students. The study objectives were (a) to explore the experiences of participating in simulation education for a small group of student nurses; and (b) to explore learning through simulation from the perspectives of the nursing students, the nurse educators and the nurse mentors. Conducted as a small-scale narrative case study, it tells the unique stories of a small number of undergraduate nursing students, nurse mentors and nurse educators and explores their experiences of learning through simulation. Data analysis through progressive focusing revealed that the nurse educators viewed simulation as a means of helping students to learn to be nurses, whilst, the nurse mentors suggested that simulation helped them to determine nursing potential. The students' narratives showed that they approached simulation learning in different ways resulting in a range of outcomes: those who were successfully becoming nurses, those who were struggling or working hard to become nurses and those who were not becoming nurses. Theories of professional practice learning and activity theory present an opportunity to articulate and theorise the learning inherent in simulation activities. They recognise the links between learning and the environment of work and highlight the possibilities for learning to inspire change and innovation. PMID:24713128

  6. Actor-critic models of reinforcement learning in the basal ganglia: From natural to artificial rats

    OpenAIRE

    Khamassi, Mehdi; Lachèze, Loïc; Girard, Benoît; Berthoz, Alain; Guillot, Agnès

    2005-01-01

    International audience Since 1995, numerous Actor–Critic architectures for reinforcement learning have been proposed as models of dopamine-like reinforcement learning mechanisms in the rat's basal ganglia. However, these models were usually tested in different tasks, and it is then difficult to compare their efficiency for an autonomous animat. We present here the comparison of four architectures in an animat as it per forms the same reward-seeking task. This will illustrate the consequenc...

  7. A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

    Institute of Scientific and Technical Information of China (English)

    Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin

    2012-01-01

    Several kernel-based methods for multi-task learning have been proposed,which leverage relations among tasks as regularization to enhance the overall learning accuracies.These methods assume that the tasks share the same kernel,which could limit their applications because in practice different tasks may need different kernels.The main challenge of introducing multiple kernels into multiple tasks is that models from different reproducing kernel Hilbert spaces (RKHSs) are not comparable,making it difficult to exploit relations among tasks.This paper addresses the challenge by formalizing the problem in the square integrable space (SIS).Specially,it proposes a kernel-based method which makes use of a regularization term defined in SIS to represent task relations.We prove a new representer theorem for the proposed approach in SIS.We further derive a practical method for solving the learning problem and conduct consistency analysis of the method.We discuss the relationship between our method and an existing method.We also give an SVM (support vector machine)-based implementation of our method for multi-label classification.Experiments on an artificial example and two real-world datasets show that the proposed method performs better than the existing method.

  8. A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

    Indian Academy of Sciences (India)

    A Stanley Raj; Y Srinivas; D Hudson Oliver; D Muthuraj

    2014-03-01

    The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

  9. Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language.

    Science.gov (United States)

    Cho, Pyeong Whan; Szkudlarek, Emily; Tabor, Whitney

    2016-01-01

    Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, a (n) b (n) , and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive

  10. Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language.

    Science.gov (United States)

    Cho, Pyeong Whan; Szkudlarek, Emily; Tabor, Whitney

    2016-01-01

    Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, a (n) b (n) , and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive

  11. Curriculum as a support to investigative approach in learning chemistry

    Directory of Open Access Journals (Sweden)

    Tomašević Biljana

    2009-01-01

    Full Text Available One of the main reasons for low achievement of our students in international tests is the lack of functional, applicable knowledge. Formation of such knowledge demands changing the usual way of implementation of instruction (transfer of ready-made knowledge to learning through performing simple research and practical work. Considering the fact that instruction, as an organised process, takes place in frameworks determined in advance, which are arranged and regulated on the national level by curricula, it is assumed that this kind of approach must originate precisely from curricula, which is not the case in our educational practice. The goal of this paper was to determine the way in which this kind of approach in instruction and learning of chemistry can be supported by the curriculum, in order for it to become a part of regular teaching practice on the national level. The paper presents how different structural components of curricula from eight different educational systems (four European countries, one Asian country, two American federal states and one Canadian province are used to promote and support the importance of research work in instruction and learning of chemistry. The curricula from Slovenia, England, Denmark, Malta, Singapore, North Carolina, Utah and Ontario were analyzed in order to determine the kind of information they offer within structural components and accordingly, the way in which each component promotes research approach to learning chemistry, how it guides the teacher in planning such activities in the classroom, organization and performing instruction, monitoring and evaluating students' achievements.

  12. A new approach for estimation of PVT properties of pure gases based on artificial neural network model

    Directory of Open Access Journals (Sweden)

    A. R. Moghadassi

    2009-03-01

    Full Text Available Equations of state are useful for description of fluid properties such as pressure-volume-temperature (PVT. However, the success estimation of such correlations depends mainly on the range of data which have originated. Therefore new models are highly required. In this work a new method is proposed based on Artificial Neural Network (ANN for estimation of PVT properties of compounds. The data sets were collected from Perry's Chemical Engineers' Handbook. Different training schemes for the back-propagation learning algorithm, such as; Scaled Conjugate Gradient (SCG, Levenberg-Marquardt (LM and Resilient back Propagation (RP methods were used. The accuracy and trend stability of the trained networks were tested against unseen data. The LM algorithm with sixty neurons in the hidden layer has proved to be the best suitable algorithm with the minimum Mean Square Error (MSE of 0.000606. The ANN's capability to estimate the PVT properties is one of the best estimating method with high performance.

  13. Changing Students' Approaches to Learning: A Two-Year Study within a University Teacher Training Course

    Science.gov (United States)

    Gijbels, David; Coertjens, Liesje; Vanthournout, Gert; Struyf, Elke; Van Petegem, Peter

    2009-01-01

    Inciting a deep approach to learning in students is difficult. The present research poses two questions: can a constructivist learning-assessment environment change students' approaches towards a more deep approach? What effect does additional feedback have on the changes in learning approaches? Two cohorts of students completed questionnaires…

  14. Multi-perspective Approaches of Vocabulary Teaching and Learning

    Institute of Scientific and Technical Information of China (English)

    王欣

    2016-01-01

    It is universally acknowledged that vocabulary is an essential component in language system. Nevertheless, in English teaching practice, imparting grammatical knowledge is highly emphasized but the vocabulary teaching is given little attention. In second language acquisition, proper application of vocabulary in communication is one of the important and difficult points for students. The paper aims to discuss the current problems in vocabulary teaching and learning, advocate a multi-perspective approach in teaching vocabulary so as to enhance the accuracy and fluency of language output, promote students’pragmatic and cross-cultural communicative competence and lay a solid foundation for their life-long learning.

  15. Statistic Approach versus Artificial Intelligence for Rainfall Prediction Based on Data Series

    Directory of Open Access Journals (Sweden)

    Indrabayu

    2013-04-01

    Full Text Available This paper proposed a new idea in comparing two common predictors i.e. the statistic method and artificial intelligence (AI for rainfall prediction using empirical data series. The statistic method uses Auto- Regressive Integrated Moving (ARIMA and Adaptive Splines Threshold Autoregressive (ASTAR, most favorable statistic tools, while in the AI, combination of Genetic Algorithm-Neural Network (GA-NN is chosen. The results show that ASTAR gives best prediction compare to others, in term of root mean square (RMSE and following trend between prediction and actual.

  16. Science of learning is learning of science: why we need a dialectical approach to science education research

    Science.gov (United States)

    Roth, Wolff-Michael

    2012-06-01

    Research on learning science in informal settings and the formal (sometimes experimental) study of learning in classrooms or psychological laboratories tend to be separate domains, even drawing on different theories and methods. These differences make it difficult to compare knowing and learning observed in one paradigm/context with those observed in the other. Even more interestingly, the scientists studying science learning rarely consider their own learning in relation to the phenomena they study. A dialectical, reflexive approach to learning, however, would theorize the movement of an educational science (its learning and development) as a special and general case—subject matter and method—of the phenomenon of learning (in/of) science. In the dialectical approach to the study of science learning, therefore, subject matter, method, and theory fall together. This allows for a perspective in which not only disparate fields of study—school science learning and learning in everyday life—are integrated but also where the progress in the science of science learning coincides with its topic. Following the articulation of a contradictory situation on comparing learning in different settings, I describe the dialectical approach. As a way of providing a concrete example, I then trace the historical movement of my own research group as it simultaneously and alternately studied science learning in formal and informal settings. I conclude by recommending cultural-historical, dialectical approaches to learning and interaction analysis as a context for fruitful interdisciplinary research on science learning within and across different settings.

  17. Effects of the Digital Game-Development Approach on Elementary School Students' Learning Motivation, Problem Solving, and Learning Achievement

    Science.gov (United States)

    Chu, Hui-Chun; Hung, Chun-Ming

    2015-01-01

    In this study, the game-based development approach is proposed for improving the learning motivation, problem solving skills, and learning achievement of students. An experiment was conducted on a learning activity of an elementary school science course to evaluate the performance of the proposed approach. A total of 59 sixth graders from two…

  18. Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Fei Song

    2014-01-01

    Full Text Available This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

  19. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  20. A novel approach for monitoring genetically engineered microorganisms by using artificial, stable RNAs

    Science.gov (United States)

    Pitulle, C.; Hedenstierna, K. O.; Fox, G. E.

    1995-01-01

    Further improvements in technology for efficient monitoring of genetically engineered microorganisms (GEMs) in the environment are needed. Technology for monitoring rRNA is well established but has not generally been applicable to GEMs because of the lack of unique rRNA target sequences. In the work described herein, it is demonstrated that a deletion mutant of a plasmid-borne Vibrio proteolyticus 5S rRNA gene continues to accumulate to high levels in Escherichia coli although it is no longer incorporated into 70S ribosomes. This deletion construct was subsequently modified by mutagenesis to create a unique recognition site for the restriction endonuclease BstEII, into which new sequences could be readily inserted. Finally, a novel 17-nucleotide identifier sequence from Pennisetum purpureum was embedded into the construct to create an RNA identification cassette. The artificial identifier RNA, expressed from this cassette in vivo, accumulated in E. coli to levels comparable to those of wild-type 5S rRNA without being seriously detrimental to cell survival in laboratory experiments and without entering the ribosomes. These results demonstrate that artificial, stable RNAs containing sequence segments remarkably different from those present in any known rRNA can be designed and that neither the deleted sequence segment nor ribosome incorporation is essential for accumulation of an RNA product.

  1. Approaches to probabilistic model learning for mobile manipulation robots

    CERN Document Server

    Sturm, Jürgen

    2013-01-01

    Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating,...

  2. Revising a design course from a lecture approach to a project-based learning approach

    Science.gov (United States)

    Kunberger, Tanya

    2013-06-01

    In order to develop the evaluative skills necessary for successful performance of design, a senior, Geotechnical Engineering course was revised to immerse students in the complexity of the design process utilising a project-based learning (PBL) approach to instruction. The student-centred approach stresses self-directed group learning, which focuses on the process rather than the result and underscores not only the theoretical but also the practical constraints of a problem. The shift in course emphasis, to skills over concepts, results in reduced content coverage but increased student ability to independently acquire a breadth of knowledge.

  3. Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.

    Science.gov (United States)

    Gomez, R L; Gerken, L

    1999-03-01

    Four experiments used the head-turn preference procedure to assess whether infants could extract and remember information from auditory strings produced by a miniature artificial grammar. In all four experiments, infants generalized to new structure by discriminating new grammatical strings from ungrammatical ones after less than 2 min exposure to the grammar. Infants acquired specific information about the grammar as demonstrated by the ability to discriminate new grammatical strings from those with illegal endpoints (Experiment 1). Infants also discriminated new grammatical strings from those with string-internal pairwise violations (Experiments 2 and 3). Infants in Experiment 4 abstracted beyond specific word order as demonstrated by the ability to discriminate new strings produced by their training grammar from strings produced by another grammar despite a change in vocabulary between training and test. We discuss the implications of these findings for the study of language acquisition.

  4. Occupational therapy students' approaches to learning: considering the impact of culture

    OpenAIRE

    Watson, Jo; Chapman, Judith; Adams, Jo; Nila, Ummey Hamila

    2006-01-01

    Learning approaches describe the way individuals approach tasks or learning situations and are influenced by individual characteristics and specific learning contexts. Cultural factors are likely to impact on various aspects of learning, yet the literature disagrees over the extent to which culture influences approaches to learning. With increasing cultural diversity in student cohorts and the contributions of western therapists to occupational therapy programmes in developing nations, this i...

  5. Artificial Root Exudate System (ARES): a field approach to simulate tree root exudation in soils

    Science.gov (United States)

    Lopez-Sangil, Luis; Estradera-Gumbau, Eduard; George, Charles; Sayer, Emma

    2016-04-01

    The exudation of labile solutes by fine roots represents an important strategy for plants to promote soil nutrient availability in terrestrial ecosystems. Compounds exuded by roots (mainly sugars, carboxylic and amino acids) provide energy to soil microbes, thus priming the mineralization of soil organic matter (SOM) and the consequent release of inorganic nutrients into the rhizosphere. Studies in several forest ecosystems suggest that tree root exudates represent 1 to 10% of the total photoassimilated C, with exudation rates increasing markedly under elevated CO2 scenarios. Despite their importance in ecosystem functioning, we know little about how tree root exudation affect soil carbon dynamics in situ. This is mainly because there has been no viable method to experimentally control inputs of root exudates at field scale. Here, I present a method to apply artificial root exudates below the soil surface in small field plots. The artificial root exudate system (ARES) consists of a water container with a mixture of labile carbon solutes (mimicking tree root exudate rates and composition), which feeds a system of drip-tips covering an area of 1 m2. The tips are evenly distributed every 20 cm and inserted 4-cm into the soil with minimal disturbance. The system is regulated by a mechanical timer, such that artificial root exudate solution can be applied at frequent, regular daily intervals. We tested ARES from April to September 2015 (growing season) within a leaf-litter manipulation experiment ongoing in temperate deciduous woodland in the UK. Soil respiration was measured monthly, and soil samples were taken at the end of the growing season for PLFA, enzymatic activity and nutrient analyses. First results show a very rapid mineralization of the root exudate compounds and, interestingly, long-term increases in SOM respiration, with negligible effects on soil moisture levels. Large positive priming effects (2.5-fold increase in soil respiration during the growing

  6. School health approach to teaching and learning of students

    Directory of Open Access Journals (Sweden)

    Lukianova Yu.S.

    2015-01-01

    Full Text Available Purpose: disclosure of health-ways for teaching and learning of students. Material: analysis of the publications of domestic and foreign authors. Results: The article is devoted to the implementation of healthy way approach to the educational process, namely, the rational organization of training aimed at keeping the dynamics of human health, the prevention of mental fatigue and overload, increase adaptive reserves of the body of the person; intensification of teaching and learning of students (application-is controversial dialogue, training, game forms and methods of training, participation in project activities, the work of pedagogical workshops that stimulates emotional accommodation and understanding of knowledge, helps students acquire personal-relevant knowledge and experience; use of health effect of artistic and practical (music, painting activities of students. Conclusions: highlights the key towards the implementation of health-promoting approach to the educational process.

  7. Influence of open- and closed-book tests on medical students' learning approaches

    NARCIS (Netherlands)

    Heijne-Penninga, Marjolein; Kuks, Jan B. M.; Hofman, W. H. Adriaan; Cohen-Schotanus, Janke

    2008-01-01

    CONTEXT Two learning approaches are consistently distinguished in the literature: deep and surface learning. The deep learning approach is considered preferable. Open-book tests are expected to stimulate deep learning and to offer a possible way of handling the substantial growth in medical knowledg

  8. Synthesizing Technology Adoption and Learners' Approaches towards Active Learning in Higher Education

    Science.gov (United States)

    Chan, Kevin; Cheung, George; Wan, Kelvin; Brown, Ian; Luk, Green

    2015-01-01

    In understanding how active and blended learning approaches with learning technologies engagement in undergraduate education, current research models tend to undermine the effect of learners' variations, particularly regarding their styles and approaches to learning, on intention and use of learning technologies. This study contributes to further…

  9. The Epistemological Beliefs, Learning Approaches and Study Orchestrations of University Students

    Science.gov (United States)

    Rodriguez, Lourdes; Cano, Francisco

    2006-01-01

    This study examined the learning experience (learning approaches, study orchestrations and epistemological beliefs) of 388 university students. Data analysis revealed two main results. First, the different aspects of students' learning experience were related: learning approaches and epistemological beliefs (two pairs of canonical variates…

  10. Learning styles and disciplinary differences from a situated cognition approach

    Directory of Open Access Journals (Sweden)

    Ana Clara Ventura

    2013-06-01

    Full Text Available Students who enter college are invited to share language, habits and customs that may contradict your thoughts, knowledge and skills previously learned. This could be a problem for student because it could learn a new way of thinking and behaving to identify with their teachers. There are agreements in the scientific literature about the university learning of a discipline involves the appropriation of concepts and the acquisition of skills and typical learning styles. This process may be explained from situated cognition approach that consider a functional relationship between way of thinking and types of activities. The gap is what effects produces higher education on learning styles. Hence, the aim of this paper is to analyze the empirical studies that compare the learning styles of students from different disciplinary in Ibero American context. For this purpose, a descriptive review was carried out of the Scielo, Dialnet, Redalyc y Doaj data bases. Descriptors used in the research for information were the key words: learning styles, disciplinary differences and higher education. We found 9 specific empirical studies that complying with all the criteria for inclusion (time period 2000-2012, research article and university sample. However, the evidence does not sufficient to achieve a point of agreement on this problematic. These results, from a point of view scientific, could open to new research lines. On the other hand, from a pedagogical-didactic view this study allow introduce the discussion of different formats educational, can be distinguished teaching strategies towards acquisition of typical styles of own discipline or teaching strategies to strengthen with the diversity of styles preexisting of students.

  11. E-learning approaches in biometry and epidemiology

    Directory of Open Access Journals (Sweden)

    Ziegler, Andreas

    2010-01-01

    Full Text Available Education is an integral component of increasing our profession’s profile. While master and PhD level education in biostatistics and epidemiology is provided at high levels in several regions, there are parts of the world deserving proper specialized education. E-learning may be one option because traveling costs can be avoided, and the capacity of teachers can be multiplied by making appropriate use of e-learning tools. The aim of this work was to explore the availability of e-learning approaches in the areas of statistics, biometry, biostatistics, epidemiology, and genetic epidemiology by a systematic literature search and a search in databases. We identified a total of 25 courses. They differ with respect to target audience, content, amount and quality. Many of them had been developed at a time when technical aspects were the main hurdle at the stage of course development. Important hygiene and motivation factors were generally unknown at that time, and, subsequently, ignored. As a result, none of the courses provides exercises that generate individual feedback to motivate the student, and no varying degree of complexity is observed. Many courses do not fulfill modern needs for e-learning. In conclusion, the development of modern e-learning following recent didactical concepts is urgently required. Sustainability of these courses is crucial and can be best guaranteed by using available technological platforms. These allow the use of common didactical principles, robust and reliable technology.

  12. Practice Makes Perfect ? The Practice Approach in E-Learning

    OpenAIRE

    Norm Friesen

    2009-01-01

    [English] Anyone involved in e-learning is certain to have run across the word practice in connection with a number of notable expressions or phrases : communities of practice, best practices, and teaching practices among others. However, there have been few definitions or discussions that address exactly what practice is. This article provides a short overview of the practice approach, focusing first on its origin in the philosophy of Heidegger and the sociology of Bourdieu. It then provides...

  13. Communicative – Activity Approach in Learning Foreign Language

    Directory of Open Access Journals (Sweden)

    Dariga A. Bekova

    2015-01-01

    Full Text Available The article is devoted communicative method of teaching foreign languages, which is the activity character. The task of the communicative approach – to interest of students in learning a foreign language through the accumulation and improvement their knowledge and experience. The main objective this method – free orienteering training in foreign language environment and the ability to adequately react in different situations, communication.

  14. Qualitative detection of oil adulteration with machine learning approaches

    OpenAIRE

    Jin, Xiao-Bo; LU Qiang; Wang, Feng; Huo, Quan-gong

    2013-01-01

    The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant? How does it constitute? What is the main ingredient of the adulteration oil? After extracting the high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil samples, we applied the adaptive boosting with multi-class Hamming loss (AdaBoost.MH) to distinguish the oil adulte...

  15. Machine Learning Approaches : from Theory to Application in Schizophrenia

    OpenAIRE

    Elisa Veronese; Umberto Castellani; Denis Peruzzo; Marcella Bellani; Paolo Brambilla

    2013-01-01

    In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comp...

  16. Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

    Directory of Open Access Journals (Sweden)

    Maya L. Pai

    2014-06-01

    Full Text Available The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u and meridional (v winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN, the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM. The ANN is trained with input parameters spanning for 36 years (1960-1995 and tested and validated for a period of 9 years (1996-2004. It is further used to predict the rainfall for 6 years (2005-2010. The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS of the validation years.

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

    Science.gov (United States)

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

    2014-06-01

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

  18. Does instructional approach matter? How elaboration plays a crucial role in multimedia learning.

    NARCIS (Netherlands)

    Eysink, T.H.S.; Jong, de T.

    2012-01-01

    This study compared the affordances of 4 multimedia learning environments for specific learning processes. The environments covered the same domain but used different instructional approaches: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry l

  19. Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application

    Directory of Open Access Journals (Sweden)

    A. Yeşim Yayla

    2011-02-01

    Full Text Available In this study, a-three layered feed-forward backpropagation Artificial Neural Network (ANN model is developed for the supplier firms in ceramic sector on the bases of user effectiveness for using concurrent engineering method. The developed model is also questioned for its usability in the supplier evaluation process. The network's independent variables of the developed model are considered as input variables of the network and dependent variables are used as output variables. The values of these variables are determined with factor analysis. For obtaining the date set to be used in the analysis, a questionnaire form with 34 questions explaining the network's input and output variables are prepared and sent out to 52 firms active in related sector. For obtaining more accurate results from the network, the questions having factor load below 0,6 are eliminated from the analysis. With the elimination of the questions from the analysis, the answers given for 22 questions explaining 8 input variables are used for the evaluation the network's inputs, the answers given for 3 questions explaining output variables are used for the evaluation the network's outputs. The data set of the network's are divided into four equal groups with k-fold method in order to get four different alternative network structures. As a conclusion, the forecasted firm scores giving the minimum error from the network test simulation and real firm scores are found to be very close to each other, thus, it is concluded that the developed artificial neural network model can be used effectively in the supplier evaluation process.

  20. Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness

    OpenAIRE

    Baeten, Marlies; Kyndt, Eva; Struyven, Katrien; Dochy, Filip

    2010-01-01

    This review outlines encouraging and discouraging factors in stimulating the adoption of deep approaches to learning in student-centred learning environments. Both encouraging and discouraging factors can be situated in the context of the learning environment, in students’ perceptions of that context and in characteristics of the students themselves. Results show that students in different disciplines differ in the approach to learning they adopt, with students in human sciences in general sh...

  1. The relationship between learning preferences (styles and approaches) and learning outcomes among pre-clinical undergraduate medical students

    OpenAIRE

    Liew, Siaw-Cheok; Sidhu, Jagmohni; Barua, Ankur

    2015-01-01

    Background Learning styles and approaches of individual undergraduate medical students vary considerably and as a consequence, their learning needs also differ from one student to another. This study was conducted to identify different learning styles and approaches of pre-clinical, undergraduate medical students and also to determine the relationships of learning preferences with performances in the summative examinations. Methods A cross-sectional study was conducted among randomly selected...

  2. Modern approaches in deep learning for SAR ATR

    Science.gov (United States)

    Wilmanski, Michael; Kreucher, Chris; Lauer, Jim

    2016-05-01

    Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior performance for object recognition in image processing applications. This paper discusses modern training approaches adopted from the image processing literature and shows how those approaches enable significantly improved performance for synthetic aperture radar (SAR) automatic target recognition (ATR). In particular, we show how a set of novel enhancements to the learning algorithm, based on new stochastic gradient descent approaches, generate significant classification improvement over previously published results on a standard dataset called MSTAR.

  3. Artificial cognition architectures

    CERN Document Server

    Crowder, James A; Friess, Shelli A

    2013-01-01

    The goal of this book is to establish the foundation, principles, theory, and concepts that are the backbone of real, autonomous Artificial Intelligence. Presented here are some basic human intelligence concepts framed for Artificial Intelligence systems. These include concepts like Metacognition and Metamemory, along with architectural constructs for Artificial Intelligence versions of human brain functions like the prefrontal cortex. Also presented are possible hardware and software architectures that lend themselves to learning, reasoning, and self-evolution

  4. The Effect of Cooperative Learning on the Learning Approaches of Students with Different Learning Styles

    Science.gov (United States)

    Çolak, Esma

    2015-01-01

    Problem Statement: For this study, a cooperative learning process was designed in which students with different learning styles could help each other in heterogeneous groups to perform teamwork-based activities. One aspect deemed important in this context was whether the instructional environment designed to reach students with different learning…

  5. DO APPROACHES TO LEARNING AFFECT ACADEMIC PERFORMANCE OF BUSINESS ETHICS STUDENTS?

    OpenAIRE

    Zaza Eliza Mohd Redza; Suhaiza Ismail; Suhaimi Mhd. Sarif; Yusof Ismail

    2013-01-01

    The objectives of this study are to explore the approaches to learning Business Ethics course adopted by students and to examine the relationship between learning approaches and academic performances of Business Ethics course. A questionnaire survey was administered to 209 students taking Business Ethics course in a higher learning institution in Malaysia. The Approaches and Study Skills Inventory for Students (ASSIST) was used to assess the learning approaches adopted by students, whilst the...

  6. Relations between Students' Approaches to Learning, Experienced Emotions and Outcomes of Learning

    Science.gov (United States)

    Trigwell, Keith; Ellis, Robert A.; Han, Feifei

    2012-01-01

    Quantitative analyses conducted on the self-reports of first year university students suggest that there is a relationship between the ways they emotionally experience their course and the approach they take to the learning of that course. Students who more strongly experience positive emotions, such as hope and pride, and more weakly experience…

  7. A Critical Approach to Social Emotional Learning Instruction through Community-Based Service Learning

    Science.gov (United States)

    McKay-Jackson, Cassandra

    2014-01-01

    The traditional teaching of reading, writing, and arithmetic alone will not fully prepare students to lead with integrity, govern fairly, analyze problems, and work collectively with people different from themselves. Social emotional learning (SEL) has been described as one of the missing links in academic education, but a restrictive approach to…

  8. A new method to learn to start in speed skating: A differencial learning approach

    NARCIS (Netherlands)

    Savelsbergh, J.P. Geert; Kamper, J. Willemiek; Rabius, Jorine; Schöllhorn, Wolfgang

    2010-01-01

    The aim of this study was to examine whether it is possible to utilize the fluctuations in human motor behaviour to induce a self-organizing process in the athlete, which takes advantage of individual movement and learning characteristics. This recently developed approach is known as differencial le

  9. Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Saini, Lalit Mohan [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119 (India)

    2008-07-15

    Up to 7 days ahead electrical peak load forecasting has been done using feed forward neural network based on Steepest descent, Bayesian regularization, Resilient and adaptive backpropagation learning methods, by incorporating the effect of eleven weather parameters and past peak load information. To avoid trapping of network into a state of local minima, the optimization of user-defined parameters viz., learning rate and error goal has been performed. The sliding window concept has been incorporated for selection of training data set. It was then reduced as per relevant selection according to the day type and season for which the forecast is made. To reduce the dimensionality of input matrix, the Principal Component Analysis method of factor extraction or correlation analysis technique has been used and their performance has been compared. The resultant data set was used for training of three-layered neural network. In order to increase the learning speed, the weights and biases were initialized according to Nguyen and Widrow method. To avoid over fitting, early stopping of training was done at the minimum validation error. (author)

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

    Directory of Open Access Journals (Sweden)

    Wendy eAguilar

    2014-10-01

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

  11. Evolutionary artificial neural network approach for predicting properties of Cu- 15Ni-8Sn-0.4Si alloy

    Institute of Scientific and Technical Information of China (English)

    FANG Shan-feng; WANG Ming-pu; WANG Yan-hui; QI Wei-hong; LI Zhou

    2008-01-01

    A novel data mining approach, based on artificial neural network(ANN) using differential evolution(DE) training algorithm, was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy. In order to improve predictive accuracy of ANN model, the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer. The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm. The present calculated results are consistent with the experimental values, which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient. Moreover, the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu- 15Ni-8Sn-0.4Si alloy.

  12. Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

    Directory of Open Access Journals (Sweden)

    Ya-li Zhou

    2009-01-01

    Full Text Available In practical active noise control (ANC systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN-based simultaneous perturbation stochastic approximation (SPSA algorithm, which functions as a nonlinear mode-free (MF controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

  13. Evaluation of students' perception of their learning environment and approaches to learning

    Science.gov (United States)

    Valyrakis, Manousos; Cheng, Ming

    2015-04-01

    This work presents the results of two case studies designed to assess the various approaches undergraduate and postgraduate students undertake for their education. The first study describes the results and evaluation of an undergraduate course in Water Engineering which aims to develop the fundamental background knowledge of students on introductory practical applications relevant to the practice of water and hydraulic engineering. The study assesses the effectiveness of the course design and learning environment from the perception of students using a questionnaire addressing several aspects that may affect student learning, performance and satisfaction, such as students' motivation, factors to effective learning, and methods of communication and assessment. The second study investigates the effectiveness of supervisory arrangements based on the perceptions of engineering undergraduate and postgraduate students. Effective supervision requires leadership skills that are not taught in the University, yet there is rarely a chance to get feedback, evaluate this process and reflect. Even though the results are very encouraging there are significant lessons to learn in improving ones practice and develop an effective learning environment to student support and guidance. The findings from these studies suggest that students with high level of intrinsic motivation are deep learners and are also top performers in a student-centered learning environment. A supportive teaching environment with a plethora of resources and feedback made available over different platforms that address students need for direct communication and feedback has the potential to improve student satisfaction and their learning experience. Finally, incorporating a multitude of assessment methods is also important in promoting deep learning. These results have deep implications about student learning and can be used to further improve course design and delivery in the future.

  14. Icing detection from geostationary satellite data using machine learning approaches

    Science.gov (United States)

    Lee, J.; Ha, S.; Sim, S.; Im, J.

    2015-12-01

    Icing can cause a significant structural damage to aircraft during flight, resulting in various aviation accidents. Icing studies have been typically performed using two approaches: one is a numerical model-based approach and the other is a remote sensing-based approach. The model based approach diagnoses aircraft icing using numerical atmospheric parameters such as temperature, relative humidity, and vertical thermodynamic structure. This approach tends to over-estimate icing according to the literature. The remote sensing-based approach typically uses meteorological satellite/ground sensor data such as Geostationary Operational Environmental Satellite (GOES) and Dual-Polarization radar data. This approach detects icing areas by applying thresholds to parameters such as liquid water path and cloud optical thickness derived from remote sensing data. In this study, we propose an aircraft icing detection approach which optimizes thresholds for L1B bands and/or Cloud Optical Thickness (COT) from Communication, Ocean and Meteorological Satellite-Meteorological Imager (COMS MI) and newly launched Himawari-8 Advanced Himawari Imager (AHI) over East Asia. The proposed approach uses machine learning algorithms including decision trees (DT) and random forest (RF) for optimizing thresholds of L1B data and/or COT. Pilot Reports (PIREPs) from South Korea and Japan were used as icing reference data. Results show that RF produced a lower false alarm rate (1.5%) and a higher overall accuracy (98.8%) than DT (8.5% and 75.3%), respectively. The RF-based approach was also compared with the existing COMS MI and GOES-R icing mask algorithms. The agreements of the proposed approach with the existing two algorithms were 89.2% and 45.5%, respectively. The lower agreement with the GOES-R algorithm was possibly due to the high uncertainty of the cloud phase product from COMS MI.

  15. Epistasis analysis using artificial intelligence.

    Science.gov (United States)

    Moore, Jason H; Hill, Doug P

    2015-01-01

    Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

  16. Simulation of artificial vision: II. Eccentric reading of full-page text and the learning of this task.

    Science.gov (United States)

    Sommerhalder, Jörg; Rappaz, Benjamin; de Haller, Raoul; Fornos, Angélica Pérez; Safran, Avinoam B; Pelizzone, Marco

    2004-01-01

    Reading of isolated words in conditions mimicking artificial vision has been found to be a difficult but feasible task. In particular at relatively high eccentricities, a significant adaptation process was required to reach optimal performances [Vision Res. 43 (2003) 269]. The present study addressed the task of full-page reading, including page navigation under control of subject's own eye movements. Conditions of artificial vision mimicking a retinal implant were simulated by projecting stimuli with reduced information content (lines of pixelised text) onto a restricted and eccentric area of the retina. Three subjects, naïve to the task, were trained for almost two months (about 1 h/day) to read full-page texts. Subjects had to use their own eye movements to displace a 10 degrees x 7 degrees viewing window, stabilised at 15 degrees eccentricity in their lower visual field. Initial reading scores were very low for two subjects (about 13% correctly read words), and astonishingly high for the third subject (86% correctly read words). However, all of them significantly improved their performance with time, reaching close to perfect reading scores (ranging from 86% to 98% correct) at the end of the training process. Reading rates were as low as 1-5 words/min at the beginning of the experiment and increased significantly with time to 14-28 words/min. Qualitative text understanding was also estimated. We observed that reading scores of at least 85% correct were necessary to achieve 'good' text understanding. Gaze position recordings, made during the experimental sessions, demonstrated that the control of eye movements, especially the suppression of reflexive vertical saccades, constituted an important part of the overall adaptive learning process. Taken together, these results suggest that retinal implants might restore full-page text reading abilities to blind patients. About 600 stimulation contacts, distributed on an implant surface of 3 x 2 mm2, appear to be a

  17. A Blended Learning Approach to Course Design and Implementation

    Science.gov (United States)

    Hoic-Bozic, N.; Mornar, V.; Boticki, I.

    2009-01-01

    Blended learning has become an increasingly popular form of e-learning, and is particularly suitable to the process of transitioning towards e-learning from traditional forms of learning and teaching. This paper describes the use of the blended e-learning model, which is based on a mixture of collaborative learning, problem-based learning (PBL)…

  18. An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

    Institute of Scientific and Technical Information of China (English)

    Okan KARAHAN; Harun TANYILDIZI; Cengiz D. ATIS

    2008-01-01

    In this study,an artificial neural network(ANN)model for studying the strength properties of steel fiber reinforced concrete(SFRC)containing fly ash was devised.The mixtures were prepared with 0 wt%,15 wt%,and 30 wt% of fly ash,at 0 vol.%,0.5 vol.%,1.0 vol.% and 1.5 vol.% of fiber,respectively.After being cured under the standard conditions for 7,28,90 and 365 d,the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths.The pa-rameters such as the amounts of cement,fly ash replacement,sand,gravel,steel fiber,and the age of samples were selected as input variables,while the compressive and flexural strengths of the concrete were chosen as the output variables.The back propagation learning algorithm with three different variants,namely the Levenberg-Marquardt(LM),scaled conjugate gradient(SCG)and Fletcher-Powell conjugate gradient(CGF)algorithms were used in the network so that the best approach can be found.The results obtained from the model and the experiments were compared,and it was found that the suitable algorithm is the LM algorithm.Furthermore,the analysis of variance(ANOVA)method was used to determine how importantly the experimental parameters affect the strength of these mixtures.

  19. Artificial neural network approach for atomic coordinate prediction of carbon nanotubes

    Science.gov (United States)

    Acı, Mehmet; Avcı, Mutlu

    2016-07-01

    In this paper, four artificial neural network (ANN) models [i.e., feed-forward neural network (FFNN), function fitting neural network (FITNET), cascade-forward neural network (CFNN) and generalized regression neural network] have been developed for atomic coordinate prediction of carbon nanotubes (CNTs). The research reported in this study has two primary objectives: (1) to develop ANN prediction models that calculate atomic coordinates of CNTs instead of using any simulation software and (2) to use results of the ANN models as an initial value of atomic coordinates for reducing number of iterations in calculation process. The dataset consisting of 10,721 data samples was created by combining the atomic coordinates of elements and chiral vectors using BIOVIA Materials Studio CASTEP (CASTEP) software. All prediction models yield very low mean squared normalized error and mean absolute error rates. Multiple correlation coefficient (R) results of FITNET, FFNN and CFNN models are close to 1. Compared with CASTEP, calculation times decrease from days to minutes. It would seem possible to predict CNTs' atomic coordinates using ANN models can be successfully used instead of mathematical calculations.

  20. An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data

    Institute of Scientific and Technical Information of China (English)

    Zhuoqi CHEN; Runhe SHI; Shupeng Zhang

    2013-01-01

    A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale.In this paper,we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET.First,the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index,land surface temperature and surface net radiation) for the period 2002-2006.ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period.The validation sites covered five different vegetation types and were not involved in the ANN training.The coefficient of determination (R2) value for comparison between estimated and measured ET was 0.77,the root-meansquare error was 0.62 mm/d,and the mean residual was -0.28.The simple model developed in this paper captured the seasonal and interarnual variation features of ET on the whole.However,the accuracy of estimated ET depended on the vegetation types,among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

  1. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. Final report, August 31, 1997

    Energy Technology Data Exchange (ETDEWEB)

    Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    1998-03-01

    The primary goal of the project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlations between wells. Using the correlations and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained. Although all the components within the overall system are functioning, the integration of dynamic data may not be practical due to the single-phase flow limitations and the computationally intensive algorithms. The future work needs to concentrate on making the dynamic data integration computationally efficient.

  2. CONTEXTUAL TEACHING AND LEARNING APPROACH TO TEACHING WRITING

    Directory of Open Access Journals (Sweden)

    Intan Satriani

    2012-07-01

    Full Text Available Abstract: This article reports a study on the implementation of contextual teaching and learning approach to teaching English writing to second graders of a Junior High Shool in Bandung. The study aims to investigate the strategies of Contextual Teaching and Learning (CTL (as adapted from Crawford, 2001 and the advantages of using CTL approach. The study employed a qualitative case study research design. The data were obtained from several instruments, namely class observations, students’ interview and students’ writing products which were then analyzed using writing assessment criteria taken from Rose (2007, as cited by Emilia, 2011, p. 151. The findings revealed that the teaching writing program was successful to improve students’ recount writing skill. Specifically, they showed some improvement on schematic structure, grammar roles, and graphic features. Moreover, the data from observation, interview, and documentation of students’ text showed some benefits of CTL. These include: (1 engaging students in the writing activity; (2 increasing students’ motivation to participate actively in the writing class; (3 helping students to construct their writing; (4 helping students to solve their problems; (5 providing ways for students to discuss or interact with their friends; and (6 helping the students to summarize and reflect the lesson. Based on these findings, it is recommended that CTL be implemented in teaching writing.   Keywords: contextual teaching and learning, teaching writing

  3. Learning the Task Management Space of an Aircraft Approach Model

    Science.gov (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty

    2014-01-01

    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  4. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

    Science.gov (United States)

    Spierings, Michelle J.; ten Cate, Carel

    2016-01-01

    The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning. PMID:27325756

  5. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment.

    Science.gov (United States)

    Spierings, Michelle J; Ten Cate, Carel

    2016-07-01

    The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning. PMID:27325756

  6. Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

    Science.gov (United States)

    Broderick, Ron

    1997-01-01

    The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network

  7. Influence of open- and closed-book tests on medical students' learning approaches

    OpenAIRE

    Heijne-Penninga, Marjolein; Kuks, Jan B. M.; Hofman, W.H. Adriaan; Cohen-Schotanus, Janke

    2008-01-01

    CONTEXT Two learning approaches are consistently distinguished in the literature: deep and surface learning. The deep learning approach is considered preferable. Open-book tests are expected to stimulate deep learning and to offer a possible way of handling the substantial growth in medical knowledge. In this study we test the hypothesis that open-book tests stimulate deep learning more than closed-book tests. METHODS Medical students in Years 2 (n = 423) and 3 (n = 306) participated in this ...

  8. A Stochastic Approach for Automatic and Dynamic Modeling of Students' Learning Styles in Adaptive Educational Systems

    OpenAIRE

    Fabiano Azevedo DORÇA; Luciano Vieira LIMA; Márcia Aparecida FERNANDES; Carlos Roberto LOPES

    2012-01-01

    Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Because of the probabilistic an...

  9. A Hybrid Ensemble Learning Approach to Star-Galaxy Classification

    CERN Document Server

    Kim, Edward J; Kind, Matias Carrasco

    2015-01-01

    There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robust star-galaxy classification. To demonstrate this hybrid, ensemble approach, we combine a purely morphological classifier, a supervised machine learning method based on random forest, an unsupervised machine learning method based on self-organizing maps, and a hierarchical Bayesian template fitting method. Using data from the CFHTLenS survey, we consider different scenarios: when a high-quality training set is available with spectroscopic labels from DEEP2, SDSS, VIPERS, and VVDS, and when the demographics of sources in a low-quality training set do not match the demographics of objects in the test data set. We demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method in these scenarios. Thus, s...

  10. An artificial intelligence approach to motif discovery in protein sequences: application to steriod dehydrogenases.

    Science.gov (United States)

    Bailey, T L; Baker, M E; Elkan, C P

    1997-05-01

    MEME (Multiple Expectation-maximization for Motif Elicitation) is a unique new software tool that uses artificial intelligence techniques to discover motifs shared by a set of protein sequences in a fully automated manner. This paper is the first detailed study of the use of MEME to analyse a large, biologically relevant set of sequences, and to evaluate the sensitivity and accuracy of MEME in identifying structurally important motifs. For this purpose, we chose the short-chain alcohol dehydrogenase superfamily because it is large and phylogenetically diverse, providing a test of how well MEME can work on sequences with low amino acid similarity. Moreover, this dataset contains enzymes of biological importance, and because several enzymes have known X-ray crystallographic structures, we can test the usefulness of MEME for structural analysis. The first six motifs from MEME map onto structurally important alpha-helices and beta-strands on Streptomyces hydrogenans 20beta-hydroxysteroid dehydrogenase. We also describe MAST (Motif Alignment Search Tool), which conveniently uses output from MEME for searching databases such as SWISS-PROT and Genpept. MAST provides statistical measures that permit a rigorous evaluation of the significance of database searches with individual motifs or groups of motifs. A database search of Genpept90 by MAST with the log-odds matrix of the first six motifs obtained from MEME yields a bimodal output, demonstrating the selectivity of MAST. We show for the first time, using primary sequence analysis, that bacterial sugar epimerases are homologs of short-chain dehydrogenases. MEME and MAST will be increasingly useful as genome sequencing provides large datasets of phylogenetically divergent sequences of biomedical interest. PMID:9366496

  11. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. Annual report, October 1994--October 1995

    Energy Technology Data Exchange (ETDEWEB)

    Kerr, D.; Thompson, L.; Shenoi, S.

    1996-01-01

    The basis of this research is to apply novel techniques from Artificial Intelligence and Expert Systems in capturing, integrating and articulating key knowledge from geology, geostatistics, and petroleum engineering to develop accurate descriptions of petroleum reservoirs. The ultimate goal is to design and implement a single powerful expert system for use by small producers and independents to efficiently exploit reservoirs. The main challenge of the proposed research is to automate the generation of detailed reservoir descriptions honoring all the available soft and hard data that ranges from qualitative and semi-quantitative geological interpretations to numeric data obtained from cores, well tests, well logs and production statistics. Additional challenges are the verification and validation of the expert system, since much of the interpretation of the experts is based on extended experience in reservoir characterization. The overall project plan to design the system to create integrated reservoir descriptions begins by initially developing an AI-based methodology for producing large-scale reservoir descriptions generated interactively from geology and well test data. Parallel to this task is a second task that develops an AI-based methodology that uses facies-biased information to generate small-scale descriptions of reservoir properties such as permeability and porosity. The third task involves consolidation and integration of the large-scale and small-scale methodologies to produce reservoir descriptions honoring all the available data. The final task will be technology transfer. With this plan, the authors have carefully allocated and sequenced the activities involved in each of the tasks to promote concurrent progress towards the research objectives. Moreover, the project duties are divided among the faculty member participants. Graduate students will work in terms with faculty members.

  12. Incorporation of iodine into apatite structure: a crystal chemistry approach using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jianwei eWang

    2015-06-01

    Full Text Available Materials with apatite crystal structure provide a great potential for incorporating the long-lived radioactive iodine isotope (129I in the form of iodide (I- from nuclear waste streams. Because of its durability and potentially high iodine content, the apatite waste form can reduce iodine release rate and minimize the waste volume. Crystal structure and composition of apatite was investigated for iodide incorporation into the channel of the structure using Artificial Neural Network. A total of 86 experimentally determined apatite crystal structures of different compositions were compiled from literature, and 46 of them were used to train the networks and 42 were used to test the performance of the trained networks. The results show that the performances of the networks are satisfactory for predictions of unit cell parameters a and c and channel size of the structure. The trained and tested networks were then used to predict unknown compositions of apatite that incorporates iodide. With a crystal chemistry consideration, chemical compositions that lead to matching the size of the structural channel to the size of iodide were then predicted to be able to incorporate iodide in the structural channel. The calculations suggest that combinations of A site cations of Ag+, K+, Sr2+, Pb2+, Ba2+, and Cs+, and X site cations, mostly formed tetrahedron, of Mn5+, As5+, Cr5+, V5+, Mo5+, Si4+, Ge4+, and Re7+ are possible apatite compositions that are able to incorporate iodide. The charge balance of different apatite compositions can be achieved by multiple substitutions at a single site or coupled substitutions at both A and X sites. The results give important clues for designing experiments to synthesize new apatite compositions and also provide a fundamental understanding how iodide is incorporated in the apatite structure. This understanding can provide important insights for apatite waste forms design by optimizing the chemical composition and synthesis

  13. Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

    Science.gov (United States)

    Dağlarli, Evren; Temeltaş, Hakan

    2008-04-01

    In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

  14. Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

    Science.gov (United States)

    Erikson, Henrik; Salzmann-Erikson, Martin

    2016-01-01

    It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences. The aim of the article is to introduce the theoretical framework and assumptions behind this idea. Both robots and monsters are posthuman creations. The knowledge we present here gives ideas about how nursing science can address the postmodern, technologic, and global world to come. Monsters therefore serve as an entrance to explore technologic innovations such as AI. Analyzing when and why monsters step out of character can provide important insights into the conceptualization of caring and nursing as a science, which is important for discussing these empathic protocols, as well as more general insight into human knowledge. The relationship between caring, monsters, robotics, and AI is not as farfetched as it might seem at first glance. PMID:27455058

  15. Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

    Science.gov (United States)

    Erikson, Henrik; Salzmann-Erikson, Martin

    2016-01-01

    It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences. The aim of the article is to introduce the theoretical framework and assumptions behind this idea. Both robots and monsters are posthuman creations. The knowledge we present here gives ideas about how nursing science can address the postmodern, technologic, and global world to come. Monsters therefore serve as an entrance to explore technologic innovations such as AI. Analyzing when and why monsters step out of character can provide important insights into the conceptualization of caring and nursing as a science, which is important for discussing these empathic protocols, as well as more general insight into human knowledge. The relationship between caring, monsters, robotics, and AI is not as farfetched as it might seem at first glance.

  16. Participant Approaches to and Reflections on Learning to Play a 12-Bar Blues in an Asynchronous E-Learning Environment

    Science.gov (United States)

    Seddon, Frederick; Biasutti, Michele

    2009-01-01

    This study investigated the viability of learning to play an improvised 12-bar blues on keyboard with both hands together in an asynchronous e-learning environment. The study also sought to reveal participant approaches to and reflections on this learning experience. Participants were video-taped as they engaged with six "Blues Activities",…

  17. Examining the Roles of Blended Learning Approaches in Computer-Supported Collaborative Learning (CSCL) Environments: A Delphi Study

    Science.gov (United States)

    So, Hyo-Jeong; Bonk, Curtis J.

    2010-01-01

    In this study, a Delphi method was used to identify and predict the roles of blended learning approaches in computer-supported collaborative learning (CSCL) environments. The Delphi panel consisted of experts in online learning from different geographic regions of the world. This study discusses findings related to (a) pros and cons of blended…

  18. ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings

    NARCIS (Netherlands)

    Drachsler, Hendrik; Pecceu, Dries; Arts, Tanja; Hutten, Edwin; Rutledge, Lloyd; Van Rosmalen, Peter; Hummel, Hans; Koper, Rob

    2009-01-01

    Drachsler, H., Peccau, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H. G. K., & Koper, R. (2009). ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings. Presentation at the 2nd Workshop Mash-Up Personal Learning

  19. Using Student-Centred Learning Environments to Stimulate Deep Approaches to Learning: Factors Encouraging or Discouraging Their Effectiveness

    Science.gov (United States)

    Baeten, Marlies; Kyndt, Eva; Struyven, Katrien; Dochy, Filip

    2010-01-01

    This review outlines encouraging and discouraging factors in stimulating the adoption of deep approaches to learning in student-centred learning environments. Both encouraging and discouraging factors can be situated in the context of the learning environment, in students' perceptions of that context and in characteristics of the students…

  20. High School Students' Approaches to Learning Physics with Relationship to Epistemic Views on Physics and Conceptions of Learning Physics

    Science.gov (United States)

    Chiou, Guo-Li; Lee, Min-Hsien; Tsai, Chin-Chung

    2013-01-01

    Background and purpose: Knowing how students learn physics is a central goal of physics education. The major purpose of this study is to examine the strength of the predictive power of students' epistemic views and conceptions of learning in terms of their approaches to learning in physics. Sample, design and method: A total of 279 Taiwanese high…