Discriminative Bayesian Dictionary Learning for Classification.

Science.gov (United States)

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

Science.gov (United States)

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Discrimination Learning in Children

Science.gov (United States)

Ochocki, Thomas E.; And Others

1975-01-01

Examined the learning performance of 192 fourth-, fifth-, and sixth-grade children on either a two or four choice simultaneous color discrimination task. Compared the use of verbal reinforcement and/or punishment, under conditions of either complete or incomplete instructions. (Author/SDH)

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

Science.gov (United States)

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

"Martin Luther King Stopped Discrimination": Multi-Generational Latino Elementary Students' Perceptions of Social Issues

Science.gov (United States)

Curwen, Margie Sauceda

2011-01-01

This study explored how multi-generational, middle-class, fifth-graders from Latino families responded to classroom discussions of social issues--particularly discrimination--and draws upon sociocultural views of culture, educational theory, and sociological perspectives of immigration to provide insight into the learning experiences of one group…

Pitch discrimination learning: specificity for pitch and harmonic resolvability, and electrophysiological correlates.

Science.gov (United States)

Carcagno, Samuele; Plack, Christopher J

2011-08-01

Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed in pitch and/or spectral resolvability of their components by the cochlea, but were filtered into the same spectral region. Cortical-evoked potentials and a behavioral measure of pitch discrimination were assessed before and after training for all the four complexes. The change in these measures was compared to that of two control groups: one trained on a level discrimination task and one without any training. The behavioral results showed that learning was partly specific to both pitch and resolvability. Training with a resolved-harmonic complex improved pitch discrimination for resolved complexes more than training with an unresolved complex. However, we did not find evidence that training with an unresolved complex leads to specific learning for unresolved complexes. Training affected the P2 component of the cortical-evoked potentials, as well as a later component (250-400 ms). No significant changes were found on the mismatch negativity (MMN) component, although a separate experiment showed that this measure was sensitive to pitch changes equivalent to the pitch discriminability changes induced by training. This result suggests that pitch discrimination training affects processes not measured by the MMN, for example, processes higher in level or parallel to those involved in MMN generation.

Neural correlates of face gender discrimination learning.

Science.gov (United States)

Su, Junzhu; Tan, Qingleng; Fang, Fang

2013-04-01

Using combined psychophysics and event-related potentials (ERPs), we investigated the effect of perceptual learning on face gender discrimination and probe the neural correlates of the learning effect. Human subjects were trained to perform a gender discrimination task with male or female faces. Before and after training, they were tested with the trained faces and other faces with the same and opposite genders. ERPs responding to these faces were recorded. Psychophysical results showed that training significantly improved subjects' discrimination performance and the improvement was specific to the trained gender, as well as to the trained identities. The training effect indicates that learning occurs at two levels-the category level (gender) and the exemplar level (identity). ERP analyses showed that the gender and identity learning was associated with the N170 latency reduction at the left occipital-temporal area and the N170 amplitude reduction at the right occipital-temporal area, respectively. These findings provide evidence for the facilitation model and the sharpening model on neuronal plasticity from visual experience, suggesting a faster processing speed and a sparser representation of face induced by perceptual learning.

Visual Aversive Learning Compromises Sensory Discrimination.

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Shalev, Lee; Paz, Rony; Avidan, Galia

2018-03-14

Aversive learning is thought to modulate perceptual thresholds, which can lead to overgeneralization. However, it remains undetermined whether this modulation is domain specific or a general effect. Moreover, despite the unique role of the visual modality in human perception, it is unclear whether this aspect of aversive learning exists in this modality. The current study was designed to examine the effect of visual aversive outcomes on the perception of basic visual and auditory features. We tested the ability of healthy participants, both males and females, to discriminate between neutral stimuli, before and after visual learning. In each experiment, neutral stimuli were associated with aversive images in an experimental group and with neutral images in a control group. Participants demonstrated a deterioration in discrimination (higher discrimination thresholds) only after aversive learning. This deterioration was measured for both auditory (tone frequency) and visual (orientation and contrast) features. The effect was replicated in five different experiments and lasted for at least 24 h. fMRI neural responses and pupil size were also measured during learning. We showed an increase in neural activations in the anterior cingulate cortex, insula, and amygdala during aversive compared with neutral learning. Interestingly, the early visual cortex showed increased brain activity during aversive compared with neutral context trials, with identical visual information. Our findings imply the existence of a central multimodal mechanism, which modulates early perceptual properties, following exposure to negative situations. Such a mechanism could contribute to abnormal responses that underlie anxiety states, even in new and safe environments. SIGNIFICANCE STATEMENT Using a visual aversive-learning paradigm, we found deteriorated discrimination abilities for visual and auditory stimuli that were associated with visual aversive stimuli. We showed increased neural

An Engineered Kinetic Amplification Mechanism for Single Nucleotide Variant Discrimination by DNA Hybridization Probes.

Science.gov (United States)

Chen, Sherry Xi; Seelig, Georg

2016-04-20

Even a single-nucleotide difference between the sequences of two otherwise identical biological nucleic acids can have dramatic functional consequences. Here, we use model-guided reaction pathway engineering to quantitatively improve the performance of selective hybridization probes in recognizing single nucleotide variants (SNVs). Specifically, we build a detection system that combines discrimination by competition with DNA strand displacement-based catalytic amplification. We show, both mathematically and experimentally, that the single nucleotide selectivity of such a system in binding to single-stranded DNA and RNA is quadratically better than discrimination due to competitive hybridization alone. As an additional benefit the integrated circuit inherits the property of amplification and provides at least 10-fold better sensitivity than standard hybridization probes. Moreover, we demonstrate how the detection mechanism can be tuned such that the detection reaction is agnostic to the position of the SNV within the target sequence. in contrast, prior strand displacement-based probes designed for kinetic discrimination are highly sensitive to position effects. We apply our system to reliably discriminate between different members of the let-7 microRNA family that differ in only a single base position. Our results demonstrate the power of systematic reaction network design to quantitatively improve biotechnology.

Discriminative learning for speech recognition

CERN Document Server

He, Xiadong

2008-01-01

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-functio

Discriminant forest classification method and system

Science.gov (United States)

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Picture-Word Differences in Discrimination Learning: II. Effects of Conceptual Categories.

Science.gov (United States)

Bourne, Lyle E., Jr.; And Others

A well established finding in the discrimination learning literature is that pictures are learned more rapidly than their associated verbal labels. It was hypothesized in this study that the usual superiority of pictures over words in a discrimination list containing same-instance repetitions would disappear in a discrimination list containing…

Polarimetric SAR image classification based on discriminative dictionary learning model

Science.gov (United States)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Doped luminescent materials and particle discrimination using same

Science.gov (United States)

Doty, F. Patrick; Allendorf, Mark D; Feng, Patrick L

2014-10-07

Doped luminescent materials are provided for converting excited triplet states to radiative hybrid states. The doped materials may be used to conduct pulse shape discrimination (PSD) using luminescence generated by harvested excited triplet states. The doped materials may also be used to detect particles using spectral shape discrimination (SSD).

Conditional discrimination learning: A critique and amplification

OpenAIRE

Schrier, Allan M.; Thompson, Claudia R.

1980-01-01

Carter and Werner recently reviewed the literature on conditional discrimination learning by pigeons, which consists of studies of matching-to-sample and oddity-from-sample. They also discussed three models of such learning: the “multiple-rule” model (learning of stimulus-specific relations), the “configuration” model, and the “single-rule” model (concept learning). Although their treatment of the multiple-rule model, which seems most applicable to the pigeon data, is generally excellent, the...

Dynamic functional brain networks involved in simple visual discrimination learning.

Science.gov (United States)

Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

2014-10-01

Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

OpenAIRE

Lu, Jiasen; Kannan, Anitha; Yang, Jianwei; Parikh, Devi; Batra, Dhruv

2017-01-01

We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained generative neural dialog models (G) is that they tend to produce 'safe' and generic responses ("I don't know", "I can't tell"). In contrast, discriminative dialog models (D) th...

Does the acceptance of hybrid learning affect learning approaches in France?

Science.gov (United States)

Marco, Lionel Di; Venot, Alain; Gillois, Pierre

2017-01-01

Acceptance of a learning technology affects students' intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students' way of learning. We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions). Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (Paffected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students' time utilization, which explains their neutral intention to use the system.

Wood Quality of Acacia Hybrid and Second-Generation Acacia mangium

Directory of Open Access Journals (Sweden)

Ismail Jusoh

2013-11-01

Full Text Available Two new tree variants, namely Acacia hybrid and second-generation Acacia mangium, have been introduced in plantation forests in Sarawak, Malaysia, and their wood qualities were examined. The mean basic density of Acacia hybrid was comparable with Acacia mangium. However basic density and strength properties of second-generation A. mangium were significantly lower compared to Acacia hybrid. The mean fibre length and fibre wall thickness in the hybrid were found to be greater than that of second-generation A. mangium. Fibre diameter and fibre lumen diameter of Acacia hybrid were smaller compared to second-generation A. mangium. Runkel and slenderness ratios of Acacia hybrid and second-generation A. mangium fibres showed that they were suitable for pulp and paper production. Acacia hybrid was more resistant to Coptotermes curvignathus attack than second-generation A. mangium. A laboratory soil block test showed that Acacia hybrid and second-generation A. mangium were moderately durable timbers. In summary, marked differences in wood properties and qualities were observed between Acacia hybrid and second-generation A. mangium.

ANALYSING SOLAR-WIND HYBRID POWER GENERATING SYSTEM

Directory of Open Access Journals (Sweden)

Mustafa ENGİN

2005-02-01

Full Text Available In this paper, a solar-wind hybrid power generating, system that will be used for security lighting was designed. Hybrid system was installed and solar cells, wind turbine, battery bank, charge regulators and inverter performance values were measured through the whole year. Using measured values of overall system efficiency, reliability, demanded energy cost per kWh were calculated, and percentage of generated energy according to resources were defined. We also include in the paper a discussion of new strategies to improve hybrid power generating system performance and demanded energy cost per kWh.

Hybrid preheat/recirculating steam generator

International Nuclear Information System (INIS)

Lilly, G.P.

1985-01-01

The patent describes a hybrid preheat/recirculating steam generator for nuclear power plants. The steam generator utilizes recirculated liquid to preheat incoming liquid. In addition, the steam generator incorporates a divider so as to limit the amount of recirculating water mixed with the feedwater. (U.K.)

Assessment on Hybrid E-Learning Instrument

OpenAIRE

Intan Farahana Kamsin; Rosseni Din

2015-01-01

This study aims to improve Hybrid e-Learning 9.3. A total of 233 students of International Islamic University Malaysia, Gombak who have the experience in hybrid teaching and learning were involved as respondents. Rasch Measurement Model was used for this study. Validity analyses conducted were on (i) the compatibility of the items, (ii) mapping of items and respondents, (iii) scaling of instruments, and (iv) unidimentional items. The findings of the study show that (i) the items developed cor...

Maladaptive learning and memory in hybrids as a reproductive isolating barrier.

Science.gov (United States)

Rice, Amber M; McQuillan, Michael A

2018-05-30

Selection against hybrid offspring, or postzygotic reproductive isolation, maintains species boundaries in the face of gene flow from hybridization. In this review, we propose that maladaptive learning and memory in hybrids is an important, but overlooked form of postzygotic reproductive isolation. Although a role for learning in premating isolation has been supported, whether learning deficiencies can contribute to postzygotic isolation has rarely been tested. We argue that the novel genetic combinations created by hybridization have the potential to impact learning and memory abilities through multiple possible mechanisms, and that any displacement from optima in these traits is likely to have fitness consequences. We review evidence supporting the potential for hybridization to affect learning and memory, and evidence of links between learning abilities and fitness. Finally, we suggest several avenues for future research. Given the importance of learning for fitness, especially in novel and unpredictable environments, maladaptive learning and memory in hybrids may be an increasingly important source of postzygotic reproductive isolation. © 2018 The Author(s).

Interaction between age and perceptual similarity in olfactory discrimination learning in F344 rats: relationships with spatial learning

Science.gov (United States)

Yoder, Wendy M.; Gaynor, Leslie S.; Burke, Sara N.; Setlow, Barry; Smith, David W.; Bizon, Jennifer L.

2017-01-01

Emerging evidence suggests that aging is associated with a reduced ability to distinguish perceptually similar stimuli in one’s environment. As the ability to accurately perceive and encode sensory information is foundational for explicit memory, understanding the neurobiological underpinnings of discrimination impairments that emerge with advancing age could help elucidate the mechanisms of mnemonic decline. To this end, there is a need for preclinical approaches that robustly and reliably model age-associated perceptual discrimination deficits. Taking advantage of rodents’ exceptional olfactory abilities, the present study applied rigorous psychophysical techniques to the evaluation of discrimination learning in young and aged F344 rats. Aging did not influence odor detection thresholds or the ability to discriminate between perceptually distinct odorants. In contrast, aged rats were disproportionately impaired relative to young on problems that required discriminations between perceptually similar olfactory stimuli. Importantly, these disproportionate impairments in discrimination learning did not simply reflect a global learning impairment in aged rats, as they performed other types of difficult discriminations on par with young rats. Among aged rats, discrimination deficits were strongly associated with spatial learning deficits. These findings reveal a new, sensitive behavioral approach for elucidating the neural mechanisms of cognitive decline associated with normal aging. PMID:28259065

Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

Spatial discrimination and visual discrimination

DEFF Research Database (Denmark)

Haagensen, Annika M. J.; Grand, Nanna; Klastrup, Signe

2013-01-01

Two methods investigating learning and memory in juvenile Gottingen minipigs were evaluated for potential use in preclinical toxicity testing. Twelve minipigs were tested using a spatial hole-board discrimination test including a learning phase and two memory phases. Five minipigs were tested...... in a visual discrimination test. The juvenile minipigs were able to learn the spatial hole-board discrimination test and showed improved working and reference memory during the learning phase. Performance in the memory phases was affected by the retention intervals, but the minipigs were able to remember...... the concept of the test in both memory phases. Working memory and reference memory were significantly improved in the last trials of the memory phases. In the visual discrimination test, the minipigs learned to discriminate between the three figures presented to them within 9-14 sessions. For the memory test...

Learning for pitch and melody discrimination in congenital amusia.

Science.gov (United States)

Whiteford, Kelly L; Oxenham, Andrew J

2018-03-23

Congenital amusia is currently thought to be a life-long neurogenetic disorder in music perception, impervious to training in pitch or melody discrimination. This study provides an explicit test of whether amusic deficits can be reduced with training. Twenty amusics and 20 matched controls participated in four sessions of psychophysical training involving either pure-tone (500 Hz) pitch discrimination or a control task of lateralization (interaural level differences for bandpass white noise). Pure-tone pitch discrimination at low, medium, and high frequencies (500, 2000, and 8000 Hz) was measured before and after training (pretest and posttest) to determine the specificity of learning. Melody discrimination was also assessed before and after training using the full Montreal Battery of Evaluation of Amusia, the most widely used standardized test to diagnose amusia. Amusics performed more poorly than controls in pitch but not localization discrimination, but both groups improved with practice on the trained stimuli. Learning was broad, occurring across all three frequencies and melody discrimination for all groups, including those who trained on the non-pitch control task. Following training, 11 of 20 amusics no longer met the global diagnostic criteria for amusia. A separate group of untrained controls (n = 20), who also completed melody discrimination and pretest, improved by an equal amount as trained controls on all measures, suggesting that the bulk of learning for the control group occurred very rapidly from the pretest. Thirty-one trained participants (13 amusics) returned one year later to assess long-term maintenance of pitch and melody discrimination. On average, there was no change in performance between posttest and one-year follow-up, demonstrating that improvements on pitch- and melody-related tasks in amusics and controls can be maintained. The findings indicate that amusia is not always a life-long deficit when using the current standard

Enriching Student Learning of Astronomy in Online Courses via Hybrid Texts

Science.gov (United States)

Montgomery, M.

2016-01-01

Hybrid texts such as Horizons: Exploring the Universe, Hybrid (with CengageNOW) and Universe, Hybrid (with CengageNOW) are designed for higher education learning of astronomy in undergraduate online courses. In these hybrid texts, quiz and test bank questions have been revised to minimize easy look-up of answers by students via the Internet and discussion threads have been re-designed to allow for student selection of learning and for personalized learning, for example. By establishing connections between the student and the course content, student learning is enriched, students spend more time learning the material, student copying of answers is minimized, and student social engagement on the subject matter is increased. In this presentation, we discuss how Hybrid texts in Astronomy can increase student learning in online courses.

Discriminating the stimulus elements during human odor-taste learning: a successful analytic stance does not eliminate learning.

Science.gov (United States)

Stevenson, Richard J; Mahmut, Mehmet K

2011-10-01

Odor "sweetness" may arise from experiencing odors and tastes together, resulting in a flavor memory that is later reaccessed by the odor. Forming a flavor memory may be impaired if the taste and odor elements are apparent during exposure, suggesting that configural processing may underpin learning. Using a new procedure, participants made actual flavor discriminations for one odor-taste pair (e.g., Taste A vs. Odor X-Taste A) and mock discriminations for another (e.g., Odor Y-Taste B vs. Odor Y-Taste B). Participants, who were successful at detecting the actual flavor discriminations, demonstrated equal amounts of learning for both odor-taste pairings. These results suggest that although a capacity to discriminate flavor into its elements may be necessary to support learning, whether participants experience a configural or elemental flavor representation may not.

Multifaceted, cross-generational costs of hybridization in sibling Drosophila species.

Directory of Open Access Journals (Sweden)

Erin M Myers

Full Text Available Maladaptive hybridization, as determined by the pattern and intensity of selection against hybrid individuals, is an important factor contributing to the evolution of prezygotic reproductive isolation. To identify the consequences of hybridization between Drosophila pseudoobscura and D. persimilis, we estimated multiple fitness components for F1 hybrids and backcross progeny and used these to compare the relative fitness of parental species and their hybrids across two generations. We document many sources of intrinsic (developmental and extrinsic (ecological selection that dramatically increase the fitness costs of hybridization beyond the well-documented F1 male sterility in this model system. Our results indicate that the cost of hybridization accrues over multiple generations and reinforcement in this system is driven by selection against hybridization above and beyond the cost of hybrid male sterility; we estimate a fitness loss of >95% relative to the parental species across two generations of hybridization. Our findings demonstrate the importance of estimating hybridization costs using multiple fitness measures from multiple generations in an ecologically relevant context; so doing can reveal intense postzygotic selection against hybridization and thus, an enhanced role for reinforcement in the evolution of populations and diversification of species.

Multifaceted, cross-generational costs of hybridization in sibling Drosophila species.

Science.gov (United States)

Myers, Erin M; Harwell, Tiffany I; Yale, Elizabeth L; Lamb, Abigail M; Frankino, W Anthony

2013-01-01

Maladaptive hybridization, as determined by the pattern and intensity of selection against hybrid individuals, is an important factor contributing to the evolution of prezygotic reproductive isolation. To identify the consequences of hybridization between Drosophila pseudoobscura and D. persimilis, we estimated multiple fitness components for F1 hybrids and backcross progeny and used these to compare the relative fitness of parental species and their hybrids across two generations. We document many sources of intrinsic (developmental) and extrinsic (ecological) selection that dramatically increase the fitness costs of hybridization beyond the well-documented F1 male sterility in this model system. Our results indicate that the cost of hybridization accrues over multiple generations and reinforcement in this system is driven by selection against hybridization above and beyond the cost of hybrid male sterility; we estimate a fitness loss of >95% relative to the parental species across two generations of hybridization. Our findings demonstrate the importance of estimating hybridization costs using multiple fitness measures from multiple generations in an ecologically relevant context; so doing can reveal intense postzygotic selection against hybridization and thus, an enhanced role for reinforcement in the evolution of populations and diversification of species.

A deep learning / neuroevolution hybrid for visual control

DEFF Research Database (Denmark)

Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

2017-01-01

This paper presents a deep learning / neuroevolution hybrid approach called DLNE, which allows FPS bots to learn to aim & shoot based only on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature...... representations, while the evolving network takes those features as inputs to infer actions. The results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks trained through evolution....

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

Science.gov (United States)

Wang, Jinhua; Yang, Xi; Cai, Hongmin; Tan, Wanchang; Jin, Cangzheng; Li, Li

2016-06-07

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

Discriminative object tracking via sparse representation and online dictionary learning.

Science.gov (United States)

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

Directory of Open Access Journals (Sweden)

Muhammad Ahmad

Full Text Available Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF, in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN. The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

Hybrid chickadees are deficient in learning and memory.

Science.gov (United States)

McQuillan, Michael A; Roth, Timothy C; Huynh, Alex V; Rice, Amber M

2018-05-01

Identifying the phenotypes underlying postzygotic reproductive isolation is crucial for fully understanding the evolution and maintenance of species. One potential postzygotic isolating barrier that has rarely been examined is learning and memory ability in hybrids. Learning and memory are important fitness-related traits, especially in scatter-hoarding species, where accurate retrieval of hoarded food is vital for winter survival. Here, we test the hypothesis that learning and memory ability can act as a postzygotic isolating barrier by comparing these traits among two scatter-hoarding songbird species, black-capped (Poecile atricapillus) and Carolina chickadees (Poecile carolinensis), and their naturally occurring hybrids. In an outdoor aviary setting, we find that hybrid chickadees perform significantly worse on an associative learning spatial task and are worse at solving a novel problem compared to both parental species. Deficiencies in learning and memory abilities could therefore contribute to postzygotic reproductive isolation between chickadee species. Given the importance of learning and memory for fitness, our results suggest that these traits may play an important, but as yet overlooked, role in postzygotic reproductive isolation. © 2018 The Author(s). Evolution © 2018 The Society for the Study of Evolution.

Valence of Facial Cues Influences Sheep Learning in a Visual Discrimination Task

Directory of Open Access Journals (Sweden)

Lucille G. A. Bellegarde

2017-11-01

Full Text Available Sheep are one of the most studied farm species in terms of their ability to process information from faces, but little is known about their face-based emotion recognition abilities. We investigated (a whether sheep could use images of sheep faces taken in situation of varying valence as cues in a simultaneous discrimination task and (b whether the valence of the situation affects their learning performance. To accomplish this, we photographed faces of sheep in three situations inducing emotional states of neutral (ruminating in the home pen or negative valence (social isolation or aggressive interaction. Sheep (n = 35 first had to learn a discrimination task with colored cards. Animals that reached the learning criterion (n = 16 were then presented with pairs of images of the face of a single individual taken in the neutral situation and in one of the negative situations. Finally, sheep had to generalize what they had learned to new pairs of images of faces taken in the same situation, but of a different conspecific. All sheep that learned the discrimination task with colored cards reached the learning criterion with images of faces. Sheep that had to associate a negative image with a food reward learned faster than sheep that had to associate a neutral image with a reward. With the exception of sheep from the aggression-rewarded group, sheep generalized this discrimination to images of faces of different individuals. Our results suggest that sheep can perceive the emotional valence displayed on faces of conspecifics and that this valence affects learning processes.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

Science.gov (United States)

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Post-training depletions of basolateral amygdala serotonin fail to disrupt discrimination, retention, or reversal learning

Directory of Open Access Journals (Sweden)

G. Jesus eOchoa

2015-05-01

Full Text Available In goal-directed pursuits, the basolateral amygdala (BLA is critical in learning about changes in the value of rewards. BLA-lesioned rats show enhanced reversal learning, a task employed to measure the flexibility of response to changes in reward. Similarly, there is a trend for enhanced discrimination learning, suggesting that BLA may modulate formation of stimulus-reward associations. There is a parallel literature on the importance of serotonin (5HT in new stimulus-reward and reversal learning. Recent postulations implicate 5HT in learning from punishment. Whereas dopaminergic involvement is critical in behavioral activation and reinforcement, 5HT may be most critical for aversive processing and behavioral inhibition, complementary cognitive processes. Given these findings, a 5HT-mediated mechanism in BLA may mediate the facilitated learning observed previously. The present study investigated the effects of selective 5HT lesions in BLA using 5,7-dihydroxytryptamine (5,7-DHT versus infusions of saline (Sham on discrimination, retention, and deterministic reversal learning. Rats were required to reach an 85% correct pairwise discrimination and single reversal criterion prior to surgery. Postoperatively, rats were then tested on the 1 retention of the pretreatment discrimination pair 2 discrimination of a novel pair and 3 reversal learning performance. We found statistically comparable preoperative learning rates between groups, intact postoperative retention, and unaltered novel discrimination and reversal learning in 5,7-DHT rats. These findings suggest that 5HT in BLA is not required for formation and flexible adjustment of new stimulus-reward associations when the strategy to efficiently solve the task has already been learned. Given the complementary role of orbitofrontal cortex in reward learning and its interconnectivity with BLA, these findings add to the list of dissociable mechanisms for BLA and orbitofrontal cortex in reward learning.

Architecture for Collaborative Learning Activities in Hybrid Learning Environments

OpenAIRE

Ibáñez, María Blanca; Maroto, David; García Rueda, José Jesús; Leony, Derick; Delgado Kloos, Carlos

2012-01-01

3D virtual worlds are recognized as collaborative learning environments. However, the underlying technology is not sufficiently mature and the virtual worlds look cartoonish, unlinked to reality. Thus, it is important to enrich them with elements from the real world to enhance student engagement in learning activities. Our approach is to build learning environments where participants can either be in the real world or in its mirror world while sharing the same hybrid space in a collaborative ...

Diverse Strategies for Diverse Learners: Action Learning in a Hybrid Mode

Directory of Open Access Journals (Sweden)

Esmarie Strydom

2007-08-01

Full Text Available This paper describes an action research study during which a flexible or hybrid approach to delivering an Information and Communication Technology competency course is implemented in the preparation of student teachers. The course incorporates Web-based course-content delivery, face-to-face classroom meetings to satisfy the need for human interaction, a variety of assessment methods, as well as recognition of prior learning. The objectives are to accommodate learning diversity, make learning focused and achievable for each learner, allow for intervention if the learning outcomes are not met, and focus on and guide the learning process, i.e. teach learners how to learn. This paper reports on the perspectives and experiences of two groups of first year learners, namely student teachers who attended a hybrid ICT course and first year learners who attended an e-learning ICT course. It was found that the success rate of the hybrid mode ICT course was significantly higher than that of the similar e-learning ICT course. The hybrid mode ICT course also enabled the learners to become self-directed to a higher degree.

Reduced autobiographical memory specificity is associated with impaired discrimination learning in anxiety disorder patients

Science.gov (United States)

Lenaert, Bert; Boddez, Yannick; Vervliet, Bram; Schruers, Koen; Hermans, Dirk

2015-01-01

Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discrimination learning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discrimination learning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discrimination learning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discrimination learning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

Can theories of animal discrimination explain perceptual learning in humans?

Science.gov (United States)

Mitchell, Chris; Hall, Geoffrey

2014-01-01

We present a review of recent studies of perceptual learning conducted with nonhuman animals. The focus of this research has been to elucidate the mechanisms by which mere exposure to a pair of similar stimuli can increase the ease with which those stimuli are discriminated. These studies establish an important role for 2 mechanisms, one involving inhibitory associations between the unique features of the stimuli, the other involving a long-term habituation process that enhances the relative salience of these features. We then examine recent work investigating equivalent perceptual learning procedures with human participants. Our aim is to determine the extent to which the phenomena exhibited by people are susceptible to explanation in terms of the mechanisms revealed by the animal studies. Although we find no evidence that associative inhibition contributes to the perceptual learning effect in humans, initial detection of unique features (those that allow discrimination between 2 similar stimuli) appears to depend on an habituation process. Once the unique features have been detected, a tendency to attend to those features and to learn about their properties enhances subsequent discrimination. We conclude that the effects obtained with humans engage mechanisms additional to those seen in animals but argue that, for the most part, these have their basis in learning processes that are common to animals and people. In a final section, we discuss some implications of this analysis of perceptual learning for other aspects of experimental psychology and consider some potential applications. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

Science.gov (United States)

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hybrid and Blended Learning: Modifying Pedagogy across Path, Pace, Time, and Place

Science.gov (United States)

O'Byrne, W. Ian; Pytash, Kristine E.

2015-01-01

Hybrid or blended learning is defined as a pedagogical approach that includes a combination of face-to-face instruction with computer-mediated instruction. The terms "blended learning", "hybrid learning", and "mixed-mode learning" are used interchangeably in current research; however, in the United States,…

Hyperspectral Image Classification Using Discriminative Dictionary Learning

International Nuclear Information System (INIS)

Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z

2014-01-01

The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance

Hybrid system power generation'wind-photovoltaic' connected to the ...

African Journals Online (AJOL)

Hybrid system power generation'wind-photovoltaic' connected to the ... from Hybrid System, power delivered to or from grid and phase voltage of the inverter leg. ... Renewable Energy, Electrical Network 220 kV, Hybrid System, Solar, MPPT.

Neighbors Based Discriminative Feature Difference Learning for Kinship Verification

DEFF Research Database (Denmark)

Duan, Xiaodong; Tan, Zheng-Hua

2015-01-01

In this paper, we present a discriminative feature difference learning method for facial image based kinship verification. To transform feature difference of an image pair to be discriminative for kinship verification, a linear transformation matrix for feature difference between an image pair...... than the commonly used feature concatenation, leading to a low complexity. Furthermore, there is no positive semi-definitive constrain on the transformation matrix while there is in metric learning methods, leading to an easy solution for the transformation matrix. Experimental results on two public...... databases show that the proposed method combined with a SVM classification method outperforms or is comparable to state-of-the-art kinship verification methods. © Springer International Publishing AG, Part of Springer Science+Business Media...

Multi-level discriminative dictionary learning with application to large scale image classification.

Science.gov (United States)

Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

2015-10-01

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

International Nuclear Information System (INIS)

Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang

2012-01-01

Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

Science.gov (United States)

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

Lithological discrimination of accretionary complex (Sivas, northern Turkey) using novel hybrid color composites and field data

Science.gov (United States)

Özkan, Mutlu; Çelik, Ömer Faruk; Özyavaş, Aziz

2018-02-01

One of the most appropriate approaches to better understand and interpret geologic evolution of an accretionary complex is to make a detailed geologic map. The fact that ophiolite sequences consist of various rock types may require a unique image processing method to map each ophiolite body. The accretionary complex in the study area is composed mainly of ophiolitic and metamorphic rocks along with epi-ophiolitic sedimentary rocks. This paper attempts to map the Late Cretaceous accretionary complex in detail in northern Sivas (within İzmir-Ankara-Erzincan Suture Zone in Turkey) by the analysis of all of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands and field study. The new two hybrid color composite images yield satisfactory results in delineating peridotite, gabbro, basalt, and epi-ophiolitic sedimentary rocks of the accretionary complex in the study area. While the first hybrid color composite image consists of one principle component (PC) and two band ratios (PC1, 3/4, 4/6 in the RGB), the PC5, the original ASTER band 4 and the 3/4 band ratio images were assigned to the RGB colors to generate the second hybrid color composite image. In addition to that, the spectral indices derived from the ASTER thermal infrared (TIR) bands discriminate clearly ultramafic, siliceous, and carbonate rocks from adjacent lithologies at a regional scale. Peridotites with varying degrees of serpentinization illustrated as a single color were best identified in the spectral indices map. Furthermore, the boundaries of ophiolitic rocks based on fieldwork were outlined in detail in some parts of the study area by superimposing the resultant maps of ASTER maps on Google Earth images of finer spatial resolution. Eventually, the encouraging geologic map generated by the image analysis of ASTER data strongly correlates with lithological boundaries from a field survey.

Fronto-striatal grey matter contributions to discrimination learning in Parkinson's disease

NARCIS (Netherlands)

O'Callaghan, C.; Moustafa, A.A.; de Wit, S.; Shine, J.M.; Robbins, T.W.; Lewis, S.J.G.; Hornberger, M.

2013-01-01

Discrimination learning deficits in Parkinson's disease (PD) have been well-established. Using both behavioral patient studies and computational approaches, these deficits have typically been attributed to dopamine imbalance across the basal ganglia. However, this explanation of impaired learning in

Hybrid cycles for micro generation

International Nuclear Information System (INIS)

Campanari, S.

2000-01-01

This paper deals with the main features of two emerging technologies in the field of small-scale power generation, micro turbines and Solid Oxide Fuel Cells, discussing the extremely high potential of their combination into hybrid cycles and their possible role for distributed cogeneration [it

Self-regulated Learning in a Hybrid Science Course at a Community College

Science.gov (United States)

Manuelito, Shannon Joy

Community college students are attracted to courses with alternative delivery formats such as hybrid courses because the more flexible delivery associated with such courses provides convenience for busy students. In a hybrid course, face-to-face, structured seat time is exchanged for online components. In such courses, students take more responsibility for their learning because they assume additional responsibility for learning more of the course material on their own. Thus, self-regulated learning (SRL) behaviors have the potential to be useful for students to successfully navigate hybrid courses because the online components require exercise of more personal control over the autonomous learning situations inherent in hybrid courses. Self-regulated learning theory includes three components: metacognition, motivation, and behavioral actions. In the current study, this theoretical framework is used to examine how inducing self-regulated learning activities among students taking a hybrid course influence performance in a community college science course. The intervention for this action research study consisted of a suite of activities that engage students in self-regulated learning behaviors to foster student performance. The specific SRL activities included predicting grades, reflections on coursework and study efforts in course preparation logs, explanation of SRL procedures in response to a vignette, photo ethnography work on their personal use of SRL approaches, and a personalized study plan. A mixed method approach was employed to gather evidence for the study. Results indicate that community college students use a variety of self-regulated learning strategies to support their learning of course material. Further, engaging community college students in learning reflection activities appears to afford some students with opportunities to refine their SRL skills and influence their learning. The discussion focuses on integrating the quantitative and qualitative

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Science.gov (United States)

Wallace, Byron C; Noel-Storr, Anna; Marshall, Iain J; Cohen, Aaron M; Smalheiser, Neil R; Thomas, James

2017-11-01

Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

High Output Piezo/Triboelectric Hybrid Generator

Science.gov (United States)

Jung, Woo-Suk; Kang, Min-Gyu; Moon, Hi Gyu; Baek, Seung-Hyub; Yoon, Seok-Jin; Wang, Zhong-Lin; Kim, Sang-Woo; Kang, Chong-Yun

2015-03-01

Recently, piezoelectric and triboelectric energy harvesting devices have been developed to convert mechanical energy into electrical energy. Especially, it is well known that triboelectric nanogenerators have a simple structure and a high output voltage. However, whereas nanostructures improve the output of triboelectric generators, its fabrication process is still complicated and unfavorable in term of the large scale and long-time durability of the device. Here, we demonstrate a hybrid generator which does not use nanostructure but generates much higher output power by a small mechanical force and integrates piezoelectric generator into triboelectric generator, derived from the simultaneous use of piezoelectric and triboelectric mechanisms in one press-and-release cycle. This hybrid generator combines high piezoelectric output current and triboelectric output voltage, which produces peak output voltage of ~370 V, current density of ~12 μA.cm-2, and average power density of ~4.44 mW.cm-2. The output power successfully lit up 600 LED bulbs by the application of a 0.2 N mechanical force and it charged a 10 μF capacitor to 10 V in 25 s. Beyond energy harvesting, this work will provide new opportunities for developing a small, built-in power source in self-powered electronics such as mobile electronics.

High Output Piezo/Triboelectric Hybrid Generator

Science.gov (United States)

Jung, Woo-Suk; Kang, Min-Gyu; Moon, Hi Gyu; Baek, Seung-Hyub; Yoon, Seok-Jin; Wang, Zhong-Lin; Kim, Sang-Woo; Kang, Chong-Yun

2015-01-01

Recently, piezoelectric and triboelectric energy harvesting devices have been developed to convert mechanical energy into electrical energy. Especially, it is well known that triboelectric nanogenerators have a simple structure and a high output voltage. However, whereas nanostructures improve the output of triboelectric generators, its fabrication process is still complicated and unfavorable in term of the large scale and long-time durability of the device. Here, we demonstrate a hybrid generator which does not use nanostructure but generates much higher output power by a small mechanical force and integrates piezoelectric generator into triboelectric generator, derived from the simultaneous use of piezoelectric and triboelectric mechanisms in one press-and-release cycle. This hybrid generator combines high piezoelectric output current and triboelectric output voltage, which produces peak output voltage of ~370 V, current density of ~12 μA·cm−2, and average power density of ~4.44 mW·cm−2. The output power successfully lit up 600 LED bulbs by the application of a 0.2 N mechanical force and it charged a 10 μF capacitor to 10 V in 25 s. Beyond energy harvesting, this work will provide new opportunities for developing a small, built-in power source in self-powered electronics such as mobile electronics. PMID:25791299

Vicarious trial-and-error behavior and hippocampal cytochrome oxidase activity during Y-maze discrimination learning in the rat.

Science.gov (United States)

Hu, Dan; Xu, Xiaojuan; Gonzalez-Lima, Francisco

2006-03-01

The present study investigated whether more vicarious trial-and-error (VTE) behavior, defined by head movement from one stimulus to another at a choice point during simultaneous discriminations, led to better visual discrimination learning in a Y-maze, and whether VTE behavior was a function of the hippocampus by measuring regional brain cytochrome oxidase (C.O.) activity, an index of neuronal metabolic activity. The results showed that the more VTEs a rat made, the better the rat learned the visual discrimination. Furthermore, both learning and VTE behavior during learning were correlated to C.O. activity in the hippocampus, suggesting that the hippocampus plays a role in VTE behavior during discrimination learning.

Hybrid Teaching in Extension: Learning at the Crossroads

Science.gov (United States)

Hino, Jeff; Kahn, Cub

2016-01-01

Extension clients' learning preferences are changing, with many increasingly going online for educational content. In response, Oregon State University Extension pilot tested a training program for Extension educators to explore hybrid teaching--a methodology that could provide more flexible access to a wider audience. Hybrid teaching offers a…

Femtomolar detection of single mismatches by discriminant analysis of DNA hybridization events using gold nanoparticles.

Science.gov (United States)

Ma, Xingyi; Sim, Sang Jun

2013-03-21

Even though DNA-based nanosensors have been demonstrated for quantitative detection of analytes and diseases, hybridization events have never been numerically investigated for further understanding of DNA mediated interactions. Here, we developed a nanoscale platform with well-designed capture and detection gold nanoprobes to precisely evaluate the hybridization events. The capture gold nanoprobes were mono-laid on glass and the detection probes were fabricated via a novel competitive conjugation method. The two kinds of probes combined in a suitable orientation following the hybridization with the target. We found that hybridization efficiency was markedly dependent on electrostatic interactions between DNA strands, which can be tailored by adjusting the salt concentration of the incubation solution. Due to the much lower stability of the double helix formed by mismatches, the hybridization efficiencies of single mismatched (MMT) and perfectly matched DNA (PMT) were different. Therefore, we obtained an optimized salt concentration that allowed for discrimination of MMT from PMT without stringent control of temperature or pH. The results indicated this to be an ultrasensitive and precise nanosensor for the diagnosis of genetic diseases.

Control of hybrid fuel cell/energy storage distributed generation system against voltage sag

Energy Technology Data Exchange (ETDEWEB)

Hajizadeh, Amin; Golkar, Masoud Aliakbar [Electrical Engineering Department, K.N. Toosi University of Technology, Seyedkhandan, Dr. Shariati Ave, P.O. Box 16315-1355, Tehran (Iran)

2010-06-15

Fuel cell (FC) and energy storage (ES) based hybrid distributed power generation systems appear to be very promising for satisfying high energy and high power requirements of power quality problems in distributed generation (DG) systems. In this study, design of control strategy for hybrid fuel cell/energy storage distributed power generation system during voltage sag has been presented. The proposed control strategy allows hybrid distributed generation system works properly when a voltage disturbance occurs in distribution system and hybrid system stays connected to the main grid. Hence, modeling, controller design, and simulation study of a hybrid distributed generation system are investigated. The physical model of the fuel cell stack, energy storage and the models of power conditioning units are described. Then the control design methodology for each component of the hybrid system is proposed. Simulation results are given to show the overall system performance including active power control and voltage sag ride-through capability of the hybrid distributed generation system. (author)

Aversive reinforcement improves visual discrimination learning in free-flying honeybees.

Directory of Open Access Journals (Sweden)

Aurore Avarguès-Weber

Full Text Available BACKGROUND: Learning and perception of visual stimuli by free-flying honeybees has been shown to vary dramatically depending on the way insects are trained. Fine color discrimination is achieved when both a target and a distractor are present during training (differential conditioning, whilst if the same target is learnt in isolation (absolute conditioning, discrimination is coarse and limited to perceptually dissimilar alternatives. Another way to potentially enhance discrimination is to increase the penalty associated with the distractor. Here we studied whether coupling the distractor with a highly concentrated quinine solution improves color discrimination of both similar and dissimilar colors by free-flying honeybees. As we assumed that quinine acts as an aversive stimulus, we analyzed whether aversion, if any, is based on an aversive sensory input at the gustatory level or on a post-ingestional malaise following quinine feeding. METHODOLOGY/PRINCIPAL FINDINGS: We show that the presence of a highly concentrated quinine solution (60 mM acts as an aversive reinforcer promoting rejection of the target associated with it, and improving discrimination of perceptually similar stimuli but not of dissimilar stimuli. Free-flying bees did not use remote cues to detect the presence of quinine solution; the aversive effect exerted by this substance was mediated via a gustatory input, i.e. via a distasteful sensory experience, rather than via a post-ingestional malaise. CONCLUSION: The present study supports the hypothesis that aversion conditioning is important for understanding how and what animals perceive and learn. By using this form of conditioning coupled with appetitive conditioning in the framework of a differential conditioning procedure, it is possible to uncover discrimination capabilities that may remain otherwise unsuspected. We show, therefore, that visual discrimination is not an absolute phenomenon but can be modulated by experience.

Effects of Learning about Historical Gender Discrimination on Early Adolescents' Occupational Judgments and Aspirations

Science.gov (United States)

Pahlke, Erin; Bigler, Rebecca S.; Green, Vanessa A.

2010-01-01

To examine the consequences of learning about gender discrimination, early adolescents (n = 121, aged 10-14) were randomly assigned to receive either (a) standard biographical lessons about historical figures (standard condition) or (b) nearly identical lessons that included information about gender discrimination (discrimination condition).…

Power generation versus fuel production in light water hybrid reactors

International Nuclear Information System (INIS)

Greenspan, E.

1977-06-01

The economic potentials of fissile-fuel-producing light-water hybrid reactors (FFP-LWHR) and of fuel-self-sufficient (FSS) LWHR's are compared. A simple economic model is constructed that gives the capital investment allowed for the hybrid reactor so that the cost of electricity generated in the hybrid based energy system equals the cost of electricity generated in LWR's. The power systems considered are LWR, FSS-LWHR, and FFP-LWHR plus LWR, both with and without plutonium recycling. The economic potential of FFP-LWHR's is found superior to that of FSS-LWHR's. Moreover, LWHR's may compete, economically, with LWR's. Criteria for determining the more economical approach to hybrid fuel or power production are derived for blankets having a linear dependence between F and M. The examples considered favor the power generation rather than fuel production

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Muhammad Naveed Iqbal Qureshi

2017-09-01

Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

Species delineation and hybrid identification using diagnostic nuclear markers for Plectropomus leopardus and Plectropomus maculatus

KAUST Repository

He, Song

2018-06-01

Diagnostic molecular markers are an essential tool in the study of species’ ecology and evolution, particularly in closely related and sympatric species. Furthermore, the increasing awareness of wild-hybrids has led to a renewed interest in rapid diagnostic assays. Here, we test the ability of two mitochondrial (Cytb and COI) and two nuclear markers (ETS2 and TMO-4c4) to confidently discriminate purebred P. leopardus and P. maculatus and their first-generation hybrids. A sample of 48 purebred individuals and 91 interspecific hybrids were used in this study and their delineation confirmed using a set of microsatellite markers. Our results indicate mitochondrial markers could not distinguish even between species but both nuclear markers confidently identified species and first-generation hybrids. However, later-generation hybrids could not always be confidently identified due to on-going introgression between species. Our findings provide a robust tool to distinguish purebred individuals and interspecific hybrids in a pair of species with an unexpectedly high incidence of hybridization. The quick species discrimination abilities provided by these diagnostic markers are important for stock assessment and recruitment studies of these important fishery species.

Species delineation and hybrid identification using diagnostic nuclear markers for Plectropomus leopardus and Plectropomus maculatus

KAUST Repository

He, Song; Harrison, Hugo B.; Berumen, Michael L.

2018-01-01

Diagnostic molecular markers are an essential tool in the study of species’ ecology and evolution, particularly in closely related and sympatric species. Furthermore, the increasing awareness of wild-hybrids has led to a renewed interest in rapid diagnostic assays. Here, we test the ability of two mitochondrial (Cytb and COI) and two nuclear markers (ETS2 and TMO-4c4) to confidently discriminate purebred P. leopardus and P. maculatus and their first-generation hybrids. A sample of 48 purebred individuals and 91 interspecific hybrids were used in this study and their delineation confirmed using a set of microsatellite markers. Our results indicate mitochondrial markers could not distinguish even between species but both nuclear markers confidently identified species and first-generation hybrids. However, later-generation hybrids could not always be confidently identified due to on-going introgression between species. Our findings provide a robust tool to distinguish purebred individuals and interspecific hybrids in a pair of species with an unexpectedly high incidence of hybridization. The quick species discrimination abilities provided by these diagnostic markers are important for stock assessment and recruitment studies of these important fishery species.

Motivation, students' needs and learning outcomes: a hybrid game-based app for enhanced language learning.

Science.gov (United States)

Berns, Anke; Isla-Montes, José-Luis; Palomo-Duarte, Manuel; Dodero, Juan-Manuel

2016-01-01

In the context of European Higher Education students face an increasing focus on independent, individual learning-at the expense of face-to-face interaction. Hence learners are, all too often, not provided with enough opportunities to negotiate in the target language. The current case study aims to address this reality by going beyond conventional approaches to provide students with a hybrid game-based app, combining individual and collaborative learning opportunities. The 4-week study was carried out with 104 German language students (A1.2 CEFR) who had previously been enrolled in a first-semester A1.1 level course at a Spanish university. The VocabTrainerA1 app-designed specifically for this study-harnesses the synergy of combining individual learning tasks and a collaborative murder mystery game in a hybrid level-based architecture. By doing so, the app provides learners with opportunities to apply their language skills to real-life-like communication. The purpose of the study was twofold: on one hand we aimed to measure learner motivation, perceived usefulness and added value of hybrid game-based apps; on the other, we sought to determine their impact on language learning. To this end, we conducted focus group interviews and an anonymous Technology Acceptance Model survey (TAM). In addition, students took a pre-test and a post-test. Scores from both tests were compared with the results obtained in first-semester conventional writing tasks, with a view to measure learning outcomes. The study provides qualitative and quantitative data supporting our initial hypotheses. Our findings suggest that hybrid game-based apps like VocabTrainerA1-which seamlessly combine individual and collaborative learning tasks-motivate learners, stimulate perceived usefulness and added value, and better meet the language learning needs of today's digital natives. In terms of acceptance, outcomes and sustainability, the data indicate that hybrid game-based apps significantly improve

Performance evaluation of stand alone hybrid PV-wind generator

Energy Technology Data Exchange (ETDEWEB)

Nasir, M. N. M.; Saharuddin, N. Z.; Sulaima, M. F.; Jali, Mohd Hafiz; Bukhari, W. M.; Bohari, Z. H. [Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Melaka (Malaysia); Yahaya, M. S. [Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Melaka (Malaysia)

2015-05-15

This paper presents the performance evaluation of standalone hybrid system on Photovoltaic (PV)-Wind generator at Faculty of Electrical Engineering (FKE), UTeM. The hybrid PV-Wind in UTeM system is combining wind turbine system with the solar system and the energy capacity of this hybrid system can generate up to charge the battery and supply the LED street lighting load. The purpose of this project is to evaluate the performance of PV-Wind hybrid generator. Solar radiation meter has been used to measure the solar radiation and anemometer has been used to measure the wind speed. The effectiveness of the PV-Wind system is based on the various data that has been collected and compared between them. The result shows that hybrid system has greater reliability. Based on the solar result, the correlation coefficient shows strong relationship between the two variables of radiation and current. The reading output current followed by fluctuate of solar radiation. However, the correlation coefficient is shows moderate relationship between the two variables of wind speed and voltage. Hence, the wind turbine system in FKE show does not operate consistently to produce energy source for this hybrid system compare to PV system. When the wind system does not fully operate due to inconsistent energy source, the other system which is PV will operate and supply the load for equilibrate the extra load demand.

Performance evaluation of stand alone hybrid PV-wind generator

Science.gov (United States)

Nasir, M. N. M.; Saharuddin, N. Z.; Sulaima, M. F.; Jali, Mohd Hafiz; Bukhari, W. M.; Bohari, Z. H.; Yahaya, M. S.

2015-05-01

This paper presents the performance evaluation of standalone hybrid system on Photovoltaic (PV)-Wind generator at Faculty of Electrical Engineering (FKE), UTeM. The hybrid PV-Wind in UTeM system is combining wind turbine system with the solar system and the energy capacity of this hybrid system can generate up to charge the battery and supply the LED street lighting load. The purpose of this project is to evaluate the performance of PV-Wind hybrid generator. Solar radiation meter has been used to measure the solar radiation and anemometer has been used to measure the wind speed. The effectiveness of the PV-Wind system is based on the various data that has been collected and compared between them. The result shows that hybrid system has greater reliability. Based on the solar result, the correlation coefficient shows strong relationship between the two variables of radiation and current. The reading output current followed by fluctuate of solar radiation. However, the correlation coefficient is shows moderate relationship between the two variables of wind speed and voltage. Hence, the wind turbine system in FKE show does not operate consistently to produce energy source for this hybrid system compare to PV system. When the wind system does not fully operate due to inconsistent energy source, the other system which is PV will operate and supply the load for equilibrate the extra load demand.

Performance evaluation of stand alone hybrid PV-wind generator

International Nuclear Information System (INIS)

Nasir, M. N. M.; Saharuddin, N. Z.; Sulaima, M. F.; Jali, Mohd Hafiz; Bukhari, W. M.; Bohari, Z. H.; Yahaya, M. S.

2015-01-01

This paper presents the performance evaluation of standalone hybrid system on Photovoltaic (PV)-Wind generator at Faculty of Electrical Engineering (FKE), UTeM. The hybrid PV-Wind in UTeM system is combining wind turbine system with the solar system and the energy capacity of this hybrid system can generate up to charge the battery and supply the LED street lighting load. The purpose of this project is to evaluate the performance of PV-Wind hybrid generator. Solar radiation meter has been used to measure the solar radiation and anemometer has been used to measure the wind speed. The effectiveness of the PV-Wind system is based on the various data that has been collected and compared between them. The result shows that hybrid system has greater reliability. Based on the solar result, the correlation coefficient shows strong relationship between the two variables of radiation and current. The reading output current followed by fluctuate of solar radiation. However, the correlation coefficient is shows moderate relationship between the two variables of wind speed and voltage. Hence, the wind turbine system in FKE show does not operate consistently to produce energy source for this hybrid system compare to PV system. When the wind system does not fully operate due to inconsistent energy source, the other system which is PV will operate and supply the load for equilibrate the extra load demand

Discriminative Multi-View Interactive Image Re-Ranking.

Science.gov (United States)

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Hybrid Model for e-Learning Quality Evaluation

Directory of Open Access Journals (Sweden)

Suzana M. Savic

2012-02-01

Full Text Available E-learning is becoming increasingly important for the competitive advantage of economic organizations and higher education institutions. Therefore, it is becoming a significant aspect of quality which has to be integrated into the management system of every organization or institution. The paper examines e-learning quality characteristics, standards, criteria and indicators and presents a multi-criteria hybrid model for e-learning quality evaluation based on the method of Analytic Hierarchy Process, trend analysis, and data comparison.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

Directory of Open Access Journals (Sweden)

Ling-Li Zeng

2018-04-01

Full Text Available Background: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. Methods: A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Findings: Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. Interpretation: The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Keywords: Schizophrenia, Deep learning, Connectome, f

Linkages between motivation, self-efficacy, self-regulated learning and preferences for traditional learning environments or those with an online component

Directory of Open Access Journals (Sweden)

Daniel Auld

2010-10-01

Full Text Available This study assessed 96 law school students’ preferences for online, hybrid, or traditional learning environments, and their reasons for these preferences, learning strategies, and motivational orientations. A discriminant analysis revealed that non-traditional learning environment familiarity, self-efficacy, and employment status were the strongest predictors of preferences for non-traditional learning environments. Preferences for traditional environments were attributed to students’ familiarity and ability to engage in and foster personal interaction. Preferences for hybrid and online environments were attributed to opportunities for enhanced learning given the convenience and flexible manner in which students with time and familial constraints could access these environments.

Demand response impacts on off-grid hybrid photovoltaic-diesel generator microgrids

Directory of Open Access Journals (Sweden)

Aaron St. Leger

2015-08-01

Full Text Available Hybrid microgrids consisting of diesel generator set(s and converter based power sources, such as solar photovoltaic or wind sources, offer an alternative to generator based off-grid power systems. The hybrid approach has been shown to be economical in many off-grid applications and can result in reduced generator operation, fuel requirements, and maintenance. However, the intermittent nature of demand and renewable energy sources typically require energy storage, such as batteries, to properly operate the hybrid microgrid. These batteries increase the system cost, require proper operation and maintenance, and have been shown to be unreliable in case studies on hybrid microgrids. This work examines the impacts of leveraging demand response in a hybrid microgrid in lieu of energy storage. The study is performed by simulating two different hybrid diesel generator—PV microgrid topologies, one with a single diesel generator and one with multiple paralleled diesel generators, for a small residential neighborhood with varying levels of demand response. Various system designs are considered and the optimal design, based on cost of energy, is presented for each level of demand response. The solar resources, performance of solar PV source, performance of diesel generators, costs of system components, maintenance, and operation are modeled and simulated at a time interval of ten minutes over a twenty-five year period for both microgrid topologies. Results are quantified through cost of energy, diesel fuel requirements, and utilization of the energy sources under varying levels of demand response. The results indicate that a moderate level of demand response can have significant positive impacts to the operation of hybrid microgrids through reduced energy cost, fuel consumption, and increased utilization of PV sources.

Mismatch discrimination of lipidated DNA and LNA-probes (LiNAs) in hybridization-controlled liposome assembly

DEFF Research Database (Denmark)

Jakobsen, Ulla; Vogel, Stefan

2016-01-01

Assays for mismatch discrimination and detection of single nucleotide variations by hybridization-controlled assembly of liposomes, which do not require tedious surface chemistry, are versatile for both DNA and RNA targets. We report herein a comprehensive study on different DNA and LNA (locked...... assay in the context of mismatch discrimination and SNP detection are presented. The advantages of membrane-anchored LiNA-probes compared to chemically attached probes on solid nanoparticles (e.g. gold nanoparticles) are described. Key functionalities such as non-covalent attachment of LiNA probes...... without the need for long spacers and the inherent mobility of membrane-anchored probes in lipid-bilayer membranes will be described for several different probe designs....

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

Science.gov (United States)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

Science.gov (United States)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum.

Science.gov (United States)

Leatemia, Lukas D; Susilo, Astrid P; van Berkel, Henk

2016-12-03

To identify the student's readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students' life styles, students' perceptions of the topics, and mood, were factors influencing their self-directed learning. A hybrid problem based curriculum may not fully affect the students' self-directed learning. The curriculum system, teacher's experiences, student's background and cultural factors might contribute to the difficulties for the student's in conducting self-directed learning.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

Science.gov (United States)

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Vehicle surge detection and pathway discrimination by pedestrians who are blind: Effect of adding an alert sound to hybrid electric vehicles on performance.

Science.gov (United States)

Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2012-05-01

This study examined the effect of adding an artificially generated alert sound to a quiet vehicle on its detectability and localizability with 15 visually impaired adults. When starting from a stationary position, the hybrid electric vehicle with an alert sound was significantly more quickly and reliably detected than either the identical vehicle without such added sound or the comparable internal combustion engine vehicle. However, no significant difference was found between the vehicles in respect to how accurately the participants could discriminate the path of a given vehicle (straight vs. right turn). These results suggest that adding an artificial sound to a hybrid electric vehicle may help reduce delay in street crossing initiation by a blind pedestrian, but the benefit of such alert sound may not be obvious in determining whether the vehicle in his near parallel lane proceeds straight through the intersection or turns right in front of him.

Contextual control of attentional allocation in human discrimination learning.

Science.gov (United States)

Uengoer, Metin; Lachnit, Harald; Lotz, Anja; Koenig, Stephan; Pearce, John M

2013-01-01

In 3 human predictive learning experiments, we investigated whether the allocation of attention can come under the control of contextual stimuli. In each experiment, participants initially received a conditional discrimination for which one set of cues was trained as relevant in Context 1 and irrelevant in Context 2, and another set was relevant in Context 2 and irrelevant in Context 1. For Experiments 1 and 2, we observed that a second discrimination based on cues that had previously been trained as relevant in Context 1 during the conditional discrimination was acquired more rapidly in Context 1 than in Context 2. Experiment 3 revealed a similar outcome when new stimuli from the original dimensions were used in the test stage. Our results support the view that the associability of a stimulus can be controlled by the stimuli that accompany it.

Four-state discrimination scheme beyond the heterodyne limit

DEFF Research Database (Denmark)

Muller, C. R.; Castaneda, Mario A. Usuga; Wittmann, C.

2012-01-01

We propose and experimentally demonstrate a hybrid discrimination scheme for the quadrature phase shift keying protocol, which outperforms heterodyne detection for any signal power. The discrimination is composed of a quadrature measurement, feed forward and photon detection.......We propose and experimentally demonstrate a hybrid discrimination scheme for the quadrature phase shift keying protocol, which outperforms heterodyne detection for any signal power. The discrimination is composed of a quadrature measurement, feed forward and photon detection....

Hybrid biomass-wind power plant for reliable energy generation

International Nuclear Information System (INIS)

Perez-Navarro, A.; Alfonso, D.; Alvarez, C.; Ibanez, F.; Sanchez, C.; Segura, I.

2010-01-01

Massive implementation of renewable energy resources is a key element to reduce CO 2 emissions associated to electricity generation. Wind resources can provide an important alternative to conventional electricity generation mainly based on fossil fuels. However, wind generators are greatly affected by the restrictive operating rules of electricity markets because, as wind is naturally variable, wind generators may have serious difficulties on submitting accurate generation schedules on a day ahead basis, and on complying with scheduled obligations in real-time operation. In this paper, an innovative system combining a biomass gasification power plant, a gas storage system and stand-by generators to stabilize a generic 40 MW wind park is proposed and evaluated with real data. The wind park power production model is based on real data about power production of a Spanish wind park and a probabilistic approach to quantify fluctuations and so, power compensation needs. The hybrid wind-biomass system is analysed to obtain main hybrid system design parameters. This hybrid system can mitigate wind prediction errors and so provide a predictable source of electricity. An entire year cycle of hourly power compensations needs has been simulated deducing storage capacity, extra power needs of the biomass power plant and stand-by generation capacity to assure power compensation during critical peak hours with acceptable reliability. (author)

Training haptic stiffness discrimination: time course of learning with or without visual information and knowledge of results.

Science.gov (United States)

Teodorescu, Kinneret; Bouchigny, Sylvain; Korman, Maria

2013-08-01

In this study, we explored the time course of haptic stiffness discrimination learning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discrimination learning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

New lager yeast strains generated by interspecific hybridization.

Science.gov (United States)

Krogerus, Kristoffer; Magalhães, Frederico; Vidgren, Virve; Gibson, Brian

2015-05-01

The interspecific hybrid Saccharomyces pastorianus is the most commonly used yeast in brewery fermentations worldwide. Here, we generated de novo lager yeast hybrids by mating a domesticated and strongly flocculent Saccharomyces cerevisiae ale strain with the Saccharomyces eubayanus type strain. The hybrids were characterized with respect to the parent strains in a wort fermentation performed at temperatures typical for lager brewing (12 °C). The resulting beers were analysed for sugar and aroma compounds, while the yeasts were tested for their flocculation ability and α-glucoside transport capability. These hybrids inherited beneficial properties from both parent strains (cryotolerance, maltotriose utilization and strong flocculation) and showed apparent hybrid vigour, fermenting faster and producing beer with higher alcohol content (5.6 vs 4.5 % ABV) than the parents. Results suggest that interspecific hybridization is suitable for production of novel non-GM lager yeast strains with unique properties and will help in elucidating the evolutionary history of industrial lager yeast.

Effects of Learning about Gender Discrimination on Adolescent Girls' Attitudes toward and Interest in Science

Science.gov (United States)

Weisgram, Erica S.; Bigler, Rebecca S.

2007-01-01

Gender discrimination has contributed to the gender imbalance in scientific fields. However, research on the effects of informing adolescent girls about gender discrimination in these fields is rare and controversial. To examine the consequences of learning about gender-based occupational discrimination, adolescent girls (n= 158, ages 11 to 14)…

Hybrid E-Textbooks as Comprehensive Interactive Learning Environments

Science.gov (United States)

Ghaem Sigarchian, Hajar; Logghe, Sara; Verborgh, Ruben; de Neve, Wesley; Salliau, Frank; Mannens, Erik; Van de Walle, Rik; Schuurman, Dimitri

2018-01-01

An e-TextBook can serve as an interactive learning environment (ILE), facilitating more effective teaching and learning processes. In this paper, we propose the novel concept of an EPUB 3-based Hybrid e-TextBook, which allows for interaction between the digital and the physical world. In that regard, we first investigated the gap between the…

Robust infrared target tracking using discriminative and generative approaches

Science.gov (United States)

Asha, C. S.; Narasimhadhan, A. V.

2017-09-01

The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

International Nuclear Information System (INIS)

YangDai, Tianyi; Zhang, Li

2016-01-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

Energy Technology Data Exchange (ETDEWEB)

YangDai, Tianyi [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China); Zhang, Li, E-mail: zhangli@nuctech.com [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education (China)

2016-02-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Liquid contrabands classification based on energy dispersive X-ray diffraction and hybrid discriminant analysis

Science.gov (United States)

YangDai, Tianyi; Zhang, Li

2016-02-01

Energy dispersive X-ray diffraction (EDXRD) combined with hybrid discriminant analysis (HDA) has been utilized for classifying the liquid materials for the first time. The XRD spectra of 37 kinds of liquid contrabands and daily supplies were obtained using an EDXRD test bed facility. The unique spectra of different samples reveal XRD's capability to distinguish liquid contrabands from daily supplies. In order to create a system to detect liquid contrabands, the diffraction spectra were subjected to HDA which is the combination of principal components analysis (PCA) and linear discriminant analysis (LDA). Experiments based on the leave-one-out method demonstrate that HDA is a practical method with higher classification accuracy and lower noise sensitivity than the other methods in this application. The study shows the great capability and potential of the combination of XRD and HDA for liquid contrabands classification.

Hybrid power markets in Africa: Generation planning, procurement and contracting challenges

International Nuclear Information System (INIS)

Malgas, Isaac; Eberhard, Anton

2011-01-01

African power sectors are generally characterised by insufficient generation capacity. Reforms to address poor performances in the 1990s followed a prescribed evolution towards power markets that would allow wholesale competition amongst generators and so lead towards efficiency improvements. Despite reforms being embarked, competitive power markets have not been established in Africa; rather, the result has been the emergence of hybrid markets where state-owned generators and IPPs operate devoid of competition; and although IPPs have emerged in a number of African power sectors, many countries still do not have sufficient generation to meet their electricity demands. This paper investigates the development of private generation power projects in Africa by analysing data collected from both primary and secondary sources in four case studies of power sectors in Ghana, Cote d'Ivoire, Morocco and Tunisia. It identifies how planning and procurement challenges have lead to difficulties in adding sufficient generation capacity in a timely manner, exacerbating the problem of insufficient generation capacity in Africa. It provides suggestions as to how these frameworks could respond more effectively to the capacity challenges faced by hybrid electricity generation markets, and how broader power sector reforms should be guided to reflect the challenges of hybrid markets better. - Research highlights: → The standard model of power sector reform should no longer be used as a progress measure of power sector development in Africa and many other developing countries. → The hybrid market should in itself be recognised as an established 'model' of power sectors in Africa and many developing countries. → Planning, procurement and contracting arrangements should be shaped specifically for hybrid markets in order to address the problem of insufficient generation capacity in developing countries.

PROBABILISTIC PROGRAMMING FOR ADVANCED MACHINE LEARNING (PPAML) DISCRIMINATIVE LEARNING FOR GENERATIVE TASKS (DILIGENT)

Science.gov (United States)

2017-11-29

follows, to see the performance of the SVM Standard algorithm: python mamiStd.py --nJobs 2 --trainSize 80 where nJobs tell the computer to use ...follows: python mamiLupi.py --nJobs 2 --trainSize 80 where nJobs tell the computer to use 2 processors and trainSize tells it to run the...in the course of DARPA PPAML program. 2 INTRODUCTION As explained in Introduction , the focus of our project is to enable the use of discriminative

High affinity γPNA sandwich hybridization assay for rapid detection of short nucleic acid targets with single mismatch discrimination.

Science.gov (United States)

Goldman, Johnathan M; Zhang, Li Ang; Manna, Arunava; Armitage, Bruce A; Ly, Danith H; Schneider, James W

2013-07-08

Hybridization analysis of short DNA and RNA targets presents many challenges for detection. The commonly employed sandwich hybridization approach cannot be implemented for these short targets due to insufficient probe-target binding strengths for unmodified DNA probes. Here, we present a method capable of rapid and stable sandwich hybridization detection for 22 nucleotide DNA and RNA targets. Stable hybridization is achieved using an n-alkylated, polyethylene glycol γ-carbon modified peptide nucleic acid (γPNA) amphiphile. The γPNA's exceptionally high affinity enables stable hybridization of a second DNA-based probe to the remaining bases of the short target. Upon hybridization of both probes, an electrophoretic mobility shift is measured via interaction of the n-alkane modification on the γPNA with capillary electrophoresis running buffer containing nonionic surfactant micelles. We find that sandwich hybridization of both probes is stable under multiple binding configurations and demonstrate single base mismatch discrimination. The binding strength of both probes is also stabilized via coaxial stacking on adjacent hybridization to targets. We conclude with a discussion on the implementation of the proposed sandwich hybridization assay as a high-throughput microRNA detection method.

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

Directory of Open Access Journals (Sweden)

Michael Veale

2017-11-01

Full Text Available Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM and fairness, accountability and transparency machine learning (FATML, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm.

Food approach conditioning and discrimination learning using sound cues in benthic sharks.

Science.gov (United States)

Vila Pouca, Catarina; Brown, Culum

2018-07-01

The marine environment is filled with biotic and abiotic sounds. Some of these sounds predict important events that influence fitness while others are unimportant. Individuals can learn specific sound cues and 'soundscapes' and use them for vital activities such as foraging, predator avoidance, communication and orientation. Most research with sounds in elasmobranchs has focused on hearing thresholds and attractiveness to sound sources, but very little is known about their abilities to learn about sounds, especially in benthic species. Here we investigated if juvenile Port Jackson sharks could learn to associate a musical stimulus with a food reward, discriminate between two distinct musical stimuli, and whether individual personality traits were linked to cognitive performance. Five out of eight sharks were successfully conditioned to associate a jazz song with a food reward delivered in a specific corner of the tank. We observed repeatable individual differences in activity and boldness in all eight sharks, but these personality traits were not linked to the learning performance assays we examined. These sharks were later trained in a discrimination task, where they had to distinguish between the same jazz and a novel classical music song, and swim to opposite corners of the tank according to the stimulus played. The sharks' performance to the jazz stimulus declined to chance levels in the discrimination task. Interestingly, some sharks developed a strong side bias to the right, which in some cases was not the correct side for the jazz stimulus.

Functional discrimination of membrane proteins using machine learning techniques

Directory of Open Access Journals (Sweden)

Yabuki Yukimitsu

2008-03-01

Full Text Available Abstract Background Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane showed the accuracy of 82%. Conclusion The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.

Active Discriminative Dictionary Learning for Weather Recognition

Directory of Open Access Journals (Sweden)

Caixia Zheng

2016-01-01

Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

Effects of MK-801 on vicarious trial-and-error and reversal of olfactory discrimination learning in weanling rats.

Science.gov (United States)

Griesbach, G S; Hu, D; Amsel, A

1998-12-01

The effects of dizocilpine maleate (MK-801) on vicarious trial-and-error (VTE), and on simultaneous olfactory discrimination learning and its reversal, were observed in weanling rats. The term VTE was used by Tolman (The determiners of behavior at a choice point. Psychol. Rev. 1938;46:318-336), who described it as conflict-like behavior at a choice-point in simultaneous discrimination learning. It takes the form of head movements from one stimulus to the other, and has recently been proposed by Amsel (Hippocampal function in the rat: cognitive mapping or vicarious trial-and-error? Hippocampus, 1993;3:251-256) as related to hippocampal, nonspatial function during this learning. Weanling male rats received systemic MK-801 either 30 min before the onset of olfactory discrimination training and its reversal, or only before its reversal. The MK-801-treated animals needed significantly more sessions to acquire the discrimination and showed significantly fewer VTEs in the acquisition phase of learning. Impaired reversal learning was shown only when MK-801 was administered during the reversal-learning phase, itself, and not when it was administered throughout both phases.

Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability

International Nuclear Information System (INIS)

Chen, Hung-Cheng

2013-01-01

Highlights: ► This paper presents a methodology for the installation capacity optimization. ► Hybrid generation system is optimized by application of adaptive genetic algorithm. ► A cost investigation is made under various conditions and component characteristics. ► The optimization scheme is validated to meet the annual power load demand. -- Abstract: The aim of this work is to present an optimization methodology for the installation capacity of a stand-alone hybrid generation system, taking into consideration the cost and reliability. Firstly, on the basis of derived steady state models of a wind generator (WG), a photovoltaic array (PV), a battery and an inverter, the hybrid generation system is modeled for the purpose of capacity optimization. Secondly, the power system is analyzed for determining both the system structure and the operation control strategy. Thirdly, according to hourly weather database of wind speed, temperature and solar irradiation, annual power generation capacity is estimated for the system match design in order that an annual power load demand can be met. The capacity determination of a hybrid generation system becomes complicated as a result of the uncertainty in the renewable energy together with load demand and the nonlinearity of system components. Aimed at the power system reliability and the cost minimization, the capacity of a hybrid generation system is optimized by application of an adaptive genetic algorithm (AGA) to individual power generation units. A total cost investigation is made under various conditions, such as wind generator power curves, battery discharge depth and the loss of load probability (LOLP). At the end of this work, the capacity of a hybrid generation system is optimized at two installation sites, namely the offshore Orchid Island and Wuchi in Taiwan. The optimization scheme is validated to optimize power capacities of a photovoltaic array, a battery and a wind turbine generator with a relative

A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

Science.gov (United States)

Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

2016-01-01

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

Science.gov (United States)

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

Pitch Discrimination Learning: Specificity for Pitch and Harmonic Resolvability, and Electrophysiological Correlates

OpenAIRE

Carcagno, Samuele; Plack, Christopher J.

2011-01-01

Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed...

Generation of auroral kilometric radiation in upper hybrid wave-lower hybrid soliton interaction

International Nuclear Information System (INIS)

Pottelette, R.; Dubouloz, N.; Treumann, R.A.

1992-01-01

Sporadic bursts of auroral kilometric radiation (AKR) associated with strong bursty electrostatic turbulence in the vicinity of the lower hybrid frequency have been frequently recorded in the AKR source region by the Viking satellite. The variation time scale of these emissions is typically 1 s, long enough for lower hybrid waves to grow to amplitudes of several hundred millivolts per meter and to evolve nonlinearly into solitons. On the basis on these observations it is suggested that formation of lower hybrid solitons may play a role in the generation of AKR. A theoretical model is proposed which is based on the direct acceleration of electrons in the combined lower hybrid soliton and upper hybrid wave fields. The solitons act as sporadic, localized antennas allowing for efficient conversions of the electrostatic energy stored in upper hybrid waves into electromagnetic radiation at a frequency above the X mode cutoff. Excitation of lower hybrid waves is due to the presence of energetic electron beams in the auroral zone found to be associated with steep plasma density gradients. Upper hybrid waves can be excited by a population of energetic electrons with loss cone distributions. The power of the electromagnetic radiation obtained is only noticeable in regions where the plasma frequency is less than the electron gyrofrequency. The theory predicts spectral power densities of the order of 10 -11 to 10 -9 W m -2 Hz -1 in the source region, in good agreement with the Viking observations. The sporadic nature of the radiation derives from lower hybrid soliton collapses which occur on ∼1-s time scales

Applying a Hybrid Model: Can It Enhance Student Learning Outcomes?

Science.gov (United States)

Potter, Jodi

2015-01-01

There has been a marked increase in the use of online learning over the past decade. There remains conflict in the current body of research on the efficacy of online versus face to face learning in these environments. One resolution of these issues is the hybrid learning option which is a combination of face-to-face classroom instruction with…

Brightness discrimination learning in a Skinner box in prenatally X-irradiated rats

International Nuclear Information System (INIS)

Tamaki, Y.; Inouye, M.

1976-01-01

Male MP 1 albino rats were exposed to x-irradiation in utero at a single dose of 200 R on day 17 of gestation. The light-dark discrimination training in a Skinner box was continued until the animals attained a learning criterion of 0.80 correct response ratio for 3 consecutive days. Although during the unreinforced baseline sessions the total number of bar pressings in the irradiated animals was superior to that in the controls, performance between the control and the irradiated animals did not differ significantly in (a) the number of training days required to attain the learning criterion, (b) the total number of days on which the animals produced a correct response ratio more than 0.80, and (c) the number of consecutive days during which the correct response ratio was more than 0.75. The results obtained suggest that the irradiated animals were able to discriminate in brightness cues as well, or nearly as well, as the controls. The cortical-subcortical system mediating brightness discrimination in the irradiated animals is discussed. (author)

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Zhi He

2017-10-01

Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

Gold nano particle decorated graphene core first generation PAMAM dendrimer for label free electrochemical DNA hybridization sensing.

Science.gov (United States)

Jayakumar, K; Rajesh, R; Dharuman, V; Venkatasan, R; Hahn, J H; Pandian, S Karutha

2012-01-15

A novel first generation (G1) poly(amidoamine) dendrimer (PAMAM) with graphene core (GG1PAMAM) was synthesized for the first time. Single layer of GG1PAMAM was immobilized covalently on mercaptopropionic acid (MPA) monolayer on Au transducer. This allows cost effective and easy deposition of single layer graphene on the Au transducer surface than the advanced vacuum techniques used in the literature. Au nano particles (17.5 nm) then decorated the GG1PAMAM and used for electrochemical DNA hybridization sensing. The sensor discriminates selectively and sensitively the complementary double stranded DNA (dsDNA, hybridized), non-complementary DNA (ssDNA, un-hybridized) and single nucleotide polymorphism (SNP) surfaces. Interactions of the MPA, GG1PAMAM and the Au nano particles were characterized by Ultra Violet (UV), Fourier Transform Infrared (FTIR), Raman spectroscopy (RS), Thermo gravimetric analysis (TGA), Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), Cyclic Voltmetric (CV), Impedance spectroscopy (IS) and Differntial Pulse Voltammetry (DPV) techniques. The sensor showed linear range 1×10(-6) to 1×10(-12) M with lowest detection limit 1 pM which is 1000 times lower than G1PAMAM without graphene core. Copyright © 2011 Elsevier B.V. All rights reserved.

Generative adversarial networks for brain lesion detection

Science.gov (United States)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Development of Web-Based Learning Application for Generation Z

Science.gov (United States)

Hariadi, Bambang; Dewiyani Sunarto, M. J.; Sudarmaningtyas, Pantjawati

2016-01-01

This study aimed to develop a web-based learning application as a form of learning revolution. The form of learning revolution includes the provision of unlimited teaching materials, real time class organization, and is not limited by time or place. The implementation of this application is in the form of hybrid learning by using Google Apps for…

Solid Oxide Fuel Cell Hybrid System for Distributed Power Generation

Energy Technology Data Exchange (ETDEWEB)

Faress Rahman; Nguyen Minh

2004-01-04

This report summarizes the work performed by Hybrid Power Generation Systems, LLC (HPGS) during the July 2003 to December 2003 reporting period under Cooperative Agreement DE-FC26-01NT40779 for the U. S. Department of Energy, National Energy Technology Laboratory (DOE/NETL) entitled ''Solid Oxide Fuel Cell Hybrid System for Distributed Power Generation''. The main objective of this project is to develop and demonstrate the feasibility of a highly efficient hybrid system integrating a planar Solid Oxide Fuel Cell (SOFC) and a micro-turbine. In addition, an activity included in this program focuses on the development of an integrated coal gasification fuel cell system concept based on planar SOFC technology. Also, another activity included in this program focuses on the development of SOFC scale up strategies.

Evaluation of Hybrid and Distance Education Learning Environments in Spain

Science.gov (United States)

Ferrer-Cascales, Rosario; Walker, Scott L.; Reig-Ferrer, Abilio; Fernandez-Pascual, Maria Dolores; Albaladejo-Blazquez, Natalia

2011-01-01

This article describes the adaptation and validation of the "Distance Education Learning Environments Survey" (DELES) for use in investigating the qualities found in distance and hybrid education psycho-social learning environments in Spain. As Europe moves toward post-secondary student mobility, equanimity in access to higher education,…

Simultaneous and Sequential Feature Negative Discriminations: Elemental Learning and Occasion Setting in Human Pavlovian Conditioning

Science.gov (United States)

Baeyens, Frank; Vervliet, Bram; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Eelen, Paul

2004-01-01

Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discrimination learning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

Training with Differential Outcomes Enhances Discriminative Learning and Visuospatial Recognition Memory in Children Born Prematurely

Science.gov (United States)

Martinez, Lourdes; Mari-Beffa, Paloma; Roldan-Tapia, Dolores; Ramos-Lizana, Julio; Fuentes, Luis J.; Estevez, Angeles F.

2012-01-01

Previous studies have demonstrated that discriminative learning is facilitated when a particular outcome is associated with each relation to be learned. When this training procedure is applied (the differential outcome procedure; DOP), learning is faster and more accurate than when the more common non-differential outcome procedure is used. This…

Renewable energy technology for off-grid power generation solar hybrid system

International Nuclear Information System (INIS)

Mohd Azhar Abd Rahman

2006-01-01

Off-grid power generation is meant to supply remote or rural area, where grid connection is almost impossible in terms of cost and geography, such as island, aborigine's villages, and areas where nature preservation is concern. Harnessing an abundance renewable energy sources using versatile hybrid power systems can offer the best, least-cost alternative solution for extending modern energy services to remote and isolated communities. The conventional method for off-grid power generation is using diesel generator with a renewable energy (RE) technology utilizing solar photovoltaic, wind, biomass, biogas and/or mini/micro hydro. A hybrid technology is a combination of multiple source of energy; such as RE and diesel generator and may also include energy storage such as battery. In our design, the concept of solar hybrid system is a combination of solar with diesel genset and battery as an energy storage. The main objective of the system are to reduce the cost of operation and maintenance, cost of logistic and carbon dioxide (CO 2 ) emission. The operational concept of solar hybrid system is that solar will be the first choice of supplying load and excess energy produced will be stored in battery. Genset will be a secondary source of energy. The system is controlled by a microprocessor-based controlled to manage the energy supplied and load demand. The solar hybrid system consists of one or two diesel generator with electronic control system, lead-acid battery system, solar PV, inverter module and system controller with remote monitoring capability. The benefits of solar hybrid system are: Improved reliability, Improved energy services, reduced emissions and pollution, provide continuous power supply, increased operational life, reduced cost, and more efficient use of power. Currently, such system has been installed at Middle and Top Station of Langkawi Cable Car, Langkawi and Aborigines Village Kg Denai, Rompin, Pahang. The technology is considered new in Malaysia

Do allopatric male Calopteryx virgo damselflies learn species recognition?

Science.gov (United States)

Kuitunen, Katja; Haukilehto, Elina; Raatikainen, Kaisa J; Hakkarainen, Hanne; Miettinen, Minna; Högmander, Harri; Kotiaho, Janne S

2012-03-01

There is a growing amount of empirical evidence that premating reproductive isolation of two closely related species can be reinforced by natural selection arising from avoidance of maladaptive hybridization. However, as an alternative for this popular reinforcement theory, it has been suggested that learning to prefer conspecifics or to discriminate heterospecifics could cause a similar pattern of reinforced premating isolation, but this possibility is much less studied. Here, we report results of a field experiment in which we examined (i) whether allopatric Calopteryx virgo damselfly males that have not encountered heterospecific females of the congener C. splendens initially show discrimination, and (ii) whether C. virgo males learn to discriminate heterospecifics or learn to associate with conspecifics during repeated experimental presentation of females. Our experiment revealed that there was a statistically nonsignificant tendency for C. virgo males to show initial discrimination against heterospecific females but because we did not use sexually naïve individuals in our experiment, we were not able to separate the effect of innate or associative learning. More importantly, however, our study revealed that species discrimination might be further strengthened by learning, especially so that C. virgo males increase their association with conspecific females during repeated presentation trials. The role of learning to discriminate C. splendens females was less clear. We conclude that learning might play a role in species recognition also when individuals are not naïve but have already encountered potential conspecific mates.

Discrimination of Species and Hybrid Detection in Myriophyllum Spp.: an Introduction to Biodiversity Conservation and Invasion Avoidance

Directory of Open Access Journals (Sweden)

R Ghahramanzadeh

2014-03-01

Full Text Available Minimizing economical loss through introduction of invasive alien species (IAS in local ecosystem is one of the most important issues in biosecurity. The hybridization potential between non-indigenous and native species has raised concerns due mainly to introgression, which can cause extirpation of native species through gene contamination. In the present study, 71 samples belonging to 12 species from Myriophyllum genus were assessed in Plant Breeding group of Wageningen University. Internal transcribed spacer (ITS was used for identification of invasive species from related native and possible hybrid plants. The result showed that based on universal application, high sequence divergence and species discrimination, ITS is a powerful sequence for the identification of invasive species from related non-invasive foreign and native species. In contrast to morphological data, ITS grouped suspected hybrid plants in to M. heterophyllum and demonstrated that they have not resulted from hybridization. These observations suggest that multiple introduction and genetic recombination among different introduced genotypes or genetic pools could be reasons of non-flowering in suspected hybrid plants. Results showed that molecular markers enable to distinguish invasive plant species from their most closely related congeners. This could be helpful with enforcing a ban on important of such invasive which can help to plant ecosystem and biodiversity stability.

Dopamine modulates memory consolidation of discrimination learning in the auditory cortex.

Science.gov (United States)

Schicknick, Horst; Reichenbach, Nicole; Smalla, Karl-Heinz; Scheich, Henning; Gundelfinger, Eckart D; Tischmeyer, Wolfgang

2012-03-01

In Mongolian gerbils, the auditory cortex is critical for discriminating rising vs. falling frequency-modulated tones. Based on our previous studies, we hypothesized that dopaminergic inputs to the auditory cortex during and shortly after acquisition of the discrimination strategy control long-term memory formation. To test this hypothesis, we studied frequency-modulated tone discrimination learning of gerbils in a shuttle box GO/NO-GO procedure following differential treatments. (i) Pre-exposure of gerbils to the frequency-modulated tones at 1 day before the first discrimination training session severely impaired the accuracy of the discrimination acquired in that session during the initial trials of a second training session, performed 1 day later. (ii) Local injection of the D1/D5 dopamine receptor antagonist SCH-23390 into the auditory cortex after task acquisition caused a discrimination deficit of similar extent and time course as with pre-exposure. This effect was dependent on the dose and time point of injection. (iii) Injection of the D1/D5 dopamine receptor agonist SKF-38393 into the auditory cortex after retraining caused a further discrimination improvement at the beginning of subsequent sessions. All three treatments, which supposedly interfered with dopamine signalling during conditioning and/or retraining, had a substantial impact on the dynamics of the discrimination performance particularly at the beginning of subsequent training sessions. These findings suggest that auditory-cortical dopamine activity after acquisition of a discrimination of complex sounds and after retrieval of weak frequency-modulated tone discrimination memory further improves memory consolidation, i.e. the correct association of two sounds with their respective GO/NO-GO meaning, in support of future memory recall. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Global discriminative learning for higher-accuracy computational gene prediction.

Directory of Open Access Journals (Sweden)

Axel Bernal

2007-03-01

Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

AN INDUCTIVE, INTERACTIVE AND ADAPTIVE HYBRID PROBLEM-BASED LEARNING METHODOLOGY: APPLICATION TO STATISTICS

Directory of Open Access Journals (Sweden)

ADA ZHENG

2011-10-01

Full Text Available We have developed an innovative hybrid problem-based learning (PBL methodology. The methodology has the following distinctive features: i Each complex question was decomposed into a set of coherent finer subquestions by following the carefully designed criteria to maintain a delicate balance between guiding the students and inspiring them to think independently. This learning methodology enabled the students to solve the complex questions progressively in an inductive context. ii Facilitated by the utilization of our web-based learning systems, the teacher was able to interact with the students intensively and could allocate more teaching time to provide tailor-made feedback for individual student. The students were actively engaged in the learning activities, stimulated by the intensive interaction. iii The answers submitted by the students could be automatically consolidated in the report of the Moodle system in real-time. The teacher could adjust the teaching schedule and focus of the class to adapt to the learning progress of the students by analysing the automatically generated report and log files of the web-based learning system. As a result, the attendance rate of the students increased from about 50% to more than 90%, and the students’ learning motivation have been significantly enhanced.

Development of vicarious trial-and-error behavior in odor discrimination learning in the rat: relation to hippocampal function?

Science.gov (United States)

Hu, D; Griesbach, G; Amsel, A

1997-06-01

Previous work from our laboratory has suggested that hippocampal electrolytic lesions result in a deficit in simultaneous, black-white discrimination learning and reduce the frequency of vicarious trial-and-error (VTE) at a choice-point. VTE is a term Tolman used to describe the rat's conflict-like behavior, moving its head from one stimulus to the other at a choice point, and has been proposed as a major nonspatial feature of hippocampal function in both visual and olfactory discrimination learning. Simultaneous odor discrimination and VTE behavior were examined at three different ages. The results were that 16-day-old pups made fewer VTEs and learned much more slowly than 30- and 60-day-olds, a finding in accord with levels of hippocampal maturity in the rat.

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

NARCIS (Netherlands)

Kamiran, F.; Zliobaite, I.; Calders, T.G.K.

2013-01-01

Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train

Cost of generating tritium internal and external to a tokamak hybrid reactor

International Nuclear Information System (INIS)

Crotzer, M.E.; Heck, F.M.; Steinke, K.C.

1981-01-01

The costs associated with producing tritium internal and external to a thorium-based tokamak hybrid are estimated for a number of scenarios and the resulting impact on the symbiotic system cost of electricity calculated. For tritium generation within the hybrid, both continuous and batch production is analyzed. For external production, the lithium-bearing blanket is replaced with thorium and the tritium is generated in the client fission reactors. Continuous tritium production within the hybrid is found to increase the cost of electricity from 1.4 to 4.0 mills/kW-h. Batch tritium production can increase the cost of electricity by 10 mills/kW-h. Producing tritium outside the hybrid, and thereby enhancing client support, increases the cost of electricity from 1.8 to 4.1 mills/kW-h

Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning.

Science.gov (United States)

Kharratzadeh, Milad; Montrey, Marcel; Metz, Alex; Shultz, Thomas R

2017-02-07

Culture is considered an evolutionary adaptation that enhances reproductive fitness. A common explanation is that social learning, the learning mechanism underlying cultural transmission, enhances mean fitness by avoiding the costs of individual learning. This explanation was famously contradicted by Rogers (1988), who used a simple mathematical model to show that cheap social learning can invade a population without raising its mean fitness. He concluded that some crucial factor remained unaccounted for, which would reverse this surprising result. Here we extend this model to include a more complex environment and limited resources, where individuals cannot reliably learn everything about the environment on their own. Under such conditions, cheap social learning evolves and enhances mean fitness, via hybrid learners capable of specializing their individual learning. We then show that while spatial or social constraints hinder the evolution of hybrid learners, a novel social learning strategy, complementary copying, can mitigate these effects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiple orbital angular momentum generated by dielectric hybrid phase element

Science.gov (United States)

Wang, Xuewen; Kuchmizhak, Aleksandr; Hu, Dejiao; Li, Xiangping

2017-09-01

Vortex beam carrying multiple orbital angular momentum provides a new degree of freedom to manipulate light leading to the various exciting applications as trapping, quantum optics, information multiplexing, etc. Helical wavefront can be generated either via the geometric or the dynamic phase arising from a space-variant birefringence (q-plate) or from phase accumulation through propagation (spiral-phase-plate), respectively. Using fast direct laser writing technique we fabricate and characterize novel hybrid q-plate generating vortex beam simultaneously carrying two different high-order topological charges, which arise from the spin-orbital conversion and the azimuthal height variation of the recorded structures. We approve the versatile concept to generate multiple-OAM vortex beams combining the spin-orbital interaction and the phase accumulation in a single micro-scale device, a hybrid dielectric phase plate.

Evaluation of models generated via hybrid evolutionary algorithms ...

African Journals Online (AJOL)

2016-04-02

Apr 2, 2016 ... Evaluation of models generated via hybrid evolutionary algorithms for the prediction of Microcystis ... evolutionary algorithms (HEA) proved to be highly applica- ble to the hypertrophic reservoirs of South Africa. .... discovered and optimised using a large-scale parallel computational device and relevant soft-.

Discrimination learning and attentional set formation in a mouse model of Fragile X.

Science.gov (United States)

Casten, Kimberly S; Gray, Annette C; Burwell, Rebecca D

2011-06-01

Fragile X Syndrome is the most prevalent genetic cause of mental retardation. Selective deficits in executive function, including inhibitory control and attention, are core features of the disorder. In humans, Fragile X results from a trinucleotide repeat in the Fmr1 gene that renders it functionally silent and has been modeled in mice by targeted deletion of the Fmr1 gene. Fmr1 knockout (KO) mice recapitulate many features of Fragile X syndrome, but evidence for deficits in executive function is inconsistent. To address this issue, we trained wild-type and Fmr1 KO mice on an experimental paradigm that assesses attentional set-shifting. Mice learned to discriminate between stimuli differing in two of three perceptual dimensions. Successful discrimination required attending only to the relevant dimension, while ignoring irrelevant dimensions. Mice were trained on three discriminations in the same perceptual dimension, each followed by a reversal. This procedure normally results in the formation of an attentional set to the relevant dimension. Mice were then required to shift attention and discriminate based on a previously irrelevant perceptual dimension. Wild-type mice exhibited the increase in trials to criterion expected when shifting attention from one perceptual dimension to another. In contrast, the Fmr1 KO group failed to show the expected increase, suggesting impairment in forming an attentional set. Fmr1 KO mice also exhibited a general impairment in learning discriminations and reversals. This is the first demonstration that Fmr1 KO mice show a deficit in attentional set formation.

A perceptual learning deficit in Chinese developmental dyslexia as revealed by visual texture discrimination training.

Science.gov (United States)

Wang, Zhengke; Cheng-Lai, Alice; Song, Yan; Cutting, Laurie; Jiang, Yuzheng; Lin, Ou; Meng, Xiangzhi; Zhou, Xiaolin

2014-08-01

Learning to read involves discriminating between different written forms and establishing connections with phonology and semantics. This process may be partially built upon visual perceptual learning, during which the ability to process the attributes of visual stimuli progressively improves with practice. The present study investigated to what extent Chinese children with developmental dyslexia have deficits in perceptual learning by using a texture discrimination task, in which participants were asked to discriminate the orientation of target bars. Experiment l demonstrated that, when all of the participants started with the same initial stimulus-to-mask onset asynchrony (SOA) at 300 ms, the threshold SOA, adjusted according to response accuracy for reaching 80% accuracy, did not show a decrement over 5 days of training for children with dyslexia, whereas this threshold SOA steadily decreased over the training for the control group. Experiment 2 used an adaptive procedure to determine the threshold SOA for each participant during training. Results showed that both the group of dyslexia and the control group attained perceptual learning over the sessions in 5 days, although the threshold SOAs were significantly higher for the group of dyslexia than for the control group; moreover, over individual participants, the threshold SOA negatively correlated with their performance in Chinese character recognition. These findings suggest that deficits in visual perceptual processing and learning might, in part, underpin difficulty in reading Chinese. Copyright © 2014 John Wiley & Sons, Ltd.

Study of applying a hybrid standalone wind-photovoltaic generation system

Directory of Open Access Journals (Sweden)

Aissa Dahmani

2015-01-01

Full Text Available The purpose of this paper is the study of applying a hybrid system wind/photovoltaic to supply a community in southern Algeria. Diesel generators are always used to provide such remote regions with energy. Using renewable energy resources is a good alternative to overcome such pollutant generators. Hybrid Optimization Model for Electric Renewable (HOMER software is used to determine the economic feasibility of the proposed configuration. Assessment of renewable resources consisting in wind and solar potentials, load profile determination and sensitivity of different parameters analysis were performed. The cost of energy (COE of 0.226 $/kWh is very competitive with those found in literature.

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

OpenAIRE

Veale, M; Binns, RDP

2017-01-01

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data o...

Vegetative and adaptive traits predict different outcomes for restoration using hybrids

Directory of Open Access Journals (Sweden)

Philip Crystal

2016-11-01

Full Text Available Abstract – Hybridization has been implicated as a driver of speciation, extinction, and invasiveness, but can also provide resistant breeding stock following epidemics. However, evaluating the appropriateness of hybrids for use in restoration programs is difficult. Past the F1 generation, the proportion of a progenitor’s genome can vary widely, as can the combinations of parental genomes. Detailed genetic analysis can reveal this information, but cannot expose phenotypic alterations due to heterosis, transgressive traits, or changes in metabolism or development. In addition, because evolution is often driven by extreme individuals, decisions based on phenotypic averages of hybrid classes may have unintended results. We demonstrate a strategy to evaluate hybrids for use in restoration by visualizing hybrid phenotypes across selected groups of traits relative to both progenitor species. Specifically, we used discriminant analysis to differentiate among butternut (Juglans cinerea L., black walnut (J. nigra L., and Japanese walnut (J. ailantifolia Carr. var. cordiformis using vegetative characters and then with functional adaptive traits associated with seedling performance. When projected onto the progenitor trait space, naturally occurring hybrids (J. ×bixbyi Rehd. between butternut and Japanese walnut showed introgression towards Japanese walnut at vegetative characters but exhibited a hybrid swarm at functional traits. Both results indicate that hybrids have morphological and ecological phenotypes that distinguish them from butternut, demonstrating a lack of ecological equivalency that should not be carried into restoration breeding efforts. Despite these discrepancies, some hybrids were projected into the space occupied by butternut seedlings’ 95% confidence ellipse, signifying that some hybrids were similar at the measured traits. Determining how to consistently identify these individuals is imperative for future breeding and species

Serious Games: improving the Learning Effect with Hybrid Games

OpenAIRE

Barhaug, Martin

2017-01-01

Previous work at NTNU has sparked an interest in hybrid board games. These kinds of games combine elements in digital and board games together. This has resulted in a platform called AnyBoard, which is a platform that makes it easier for developers to create and develop hybrid board games. The platform was created at NTNU and has been worked on by students and employees at the IDI institute. This thesis aims to investigate this platform, and look at the potential it has to influence learn...

Handling conditional discrimination

NARCIS (Netherlands)

Zliobaite, I.; Kamiran, F.; Calders, T.G.K.

2011-01-01

Historical data used for supervised learning may contain discrimination. We study how to train classifiers on such data, so that they are discrimination free with respect to a given sensitive attribute, e.g., gender. Existing techniques that deal with this problem aim at removing all discrimination

Observation versus classification in supervised category learning.

Science.gov (United States)

Levering, Kimery R; Kurtz, Kenneth J

2015-02-01

The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.

Craniomandibular form and body size variation of first generation mouse hybrids: A model for hominin hybridization.

Science.gov (United States)

Warren, Kerryn A; Ritzman, Terrence B; Humphreys, Robyn A; Percival, Christopher J; Hallgrímsson, Benedikt; Ackermann, Rebecca Rogers

2018-03-01

Hybridization occurs in a number of mammalian lineages, including among primate taxa. Analyses of ancient genomes have shown that hybridization between our lineage and other archaic hominins in Eurasia occurred numerous times in the past. However, we still have limited empirical data on what a hybrid skeleton looks like, or how to spot patterns of hybridization among fossils for which there are no genetic data. Here we use experimental mouse models to supplement previous studies of primates. We characterize size and shape variation in the cranium and mandible of three wild-derived inbred mouse strains and their first generation (F 1 ) hybrids. The three parent taxa in our analysis represent lineages that diverged over approximately the same period as the human/Neanderthal/Denisovan lineages and their hybrids are variably successful in the wild. Comparisons of body size, as quantified by long bone measurements, are also presented to determine whether the identified phenotypic effects of hybridization are localized to the cranium or represent overall body size changes. The results indicate that hybrid cranial and mandibular sizes, as well as limb length, exceed that of the parent taxa in all cases. All three F 1 hybrid crosses display similar patterns of size and form variation. These results are generally consistent with earlier studies on primates and other mammals, suggesting that the effects of hybridization may be similar across very different scenarios of hybridization, including different levels of hybrid fitness. This paper serves to supplement previous studies aimed at identifying F 1 hybrids in the fossil record and to introduce further research that will explore hybrid morphologies using mice as a proxy for better understanding hybridization in the hominin fossil record. Copyright © 2017 Elsevier Ltd. All rights reserved.

SOLID OXIDE FUEL CELL HYBRID SYSTEM FOR DISTRIBUTED POWER GENERATION

Energy Technology Data Exchange (ETDEWEB)

Faress Rahman; Nguyen Minh

2003-07-01

This report summarizes the work performed by Hybrid Power Generation Systems, LLC during the January 2003 to June 2003 reporting period under Cooperative Agreement DE-FC26-01NT40779 for the U. S. Department of Energy, National Energy Technology Laboratory (DOE/NETL) entitled ''Solid Oxide Fuel Cell Hybrid System for Distributed Power Generation''. The main objective of this project is to develop and demonstrate the feasibility of a highly efficient hybrid system integrating a planar Solid Oxide Fuel Cell (SOFC) and a micro-turbine. In addition, an activity included in this program focuses on the development of an integrated coal gasification fuel cell system concept based on planar SOFC technology. This report summarizes the results obtained to date on: System performance analysis and model optimization; Reliability and cost model development; System control including dynamic model development; Heat exchanger material tests and life analysis; Pressurized SOFC evaluation; and Pre-baseline system definition for coal gasification fuel cell system concept.

Generative Learning: Adults Learning within Ambiguity

Science.gov (United States)

Nicolaides, Aliki

2015-01-01

This study explored the extent to which ambiguity can serve as a catalyst for adult learning. The purpose of this study is to understand learning that is generated when encountering ambiguity agitated by the complexity of liquid modernity. "Ambiguity," in this study, describes an encounter with an appearance of reality that is at first…

Selective increase of auditory cortico-striatal coherence during auditory-cued Go/NoGo discrimination learning.

Directory of Open Access Journals (Sweden)

Andreas L. Schulz

2016-01-01

Full Text Available Goal directed behavior and associated learning processes are tightly linked to neuronal activity in the ventral striatum. Mechanisms that integrate task relevant sensory information into striatal processing during decision making and learning are implicitly assumed in current reinforcementmodels, yet they are still weakly understood. To identify the functional activation of cortico-striatal subpopulations of connections during auditory discrimination learning, we trained Mongolian gerbils in a two-way active avoidance task in a shuttlebox to discriminate between falling and rising frequency modulated tones with identical spectral properties. We assessed functional coupling by analyzing the field-field coherence between the auditory cortex and the ventral striatum of animals performing the task. During the course of training, we observed a selective increase of functionalcoupling during Go-stimulus presentations. These results suggest that the auditory cortex functionally interacts with the ventral striatum during auditory learning and that the strengthening of these functional connections is selectively goal-directed.

Discrimination performance in aging is vulnerable to interference and dissociable from spatial memory

Science.gov (United States)

Johnson, Sarah A.; Sacks, Patricia K.; Turner, Sean M.; Gaynor, Leslie S.; Ormerod, Brandi K.; Maurer, Andrew P.; Bizon, Jennifer L.

2016-01-01

Hippocampal-dependent episodic memory and stimulus discrimination abilities are both compromised in the elderly. The reduced capacity to discriminate between similar stimuli likely contributes to multiple aspects of age-related cognitive impairment; however, the association of these behaviors within individuals has never been examined in an animal model. In the present study, young and aged F344×BN F1 hybrid rats were cross-characterized on the Morris water maze test of spatial memory and a dentate gyrus-dependent match-to-position test of spatial discrimination ability. Aged rats showed overall impairments relative to young in spatial learning and memory on the water maze task. Although young and aged learned to apply a match-to-position response strategy in performing easy spatial discriminations within a similar number of trials, a majority of aged rats were impaired relative to young in performing difficult spatial discriminations on subsequent tests. Moreover, all aged rats were susceptible to cumulative interference during spatial discrimination tests, such that error rate increased on later trials of test sessions. These data suggest that when faced with difficult discriminations, the aged rats were less able to distinguish current goal locations from those of previous trials. Increasing acetylcholine levels with donepezil did not improve aged rats’ abilities to accurately perform difficult spatial discriminations or reduce their susceptibility to interference. Interestingly, better spatial memory abilities were not significantly associated with higher performance on difficult spatial discriminations. This observation, along with the finding that aged rats made more errors under conditions in which interference was high, suggests that match-to-position spatial discrimination performance may rely on extra-hippocampal structures such as the prefrontal cortex, in addition to the dentate gyrus. PMID:27317194

Thermodynamic evaluation of a novel solar-biomass hybrid power generation system

International Nuclear Information System (INIS)

Bai, Zhang; Liu, Qibin; Lei, Jing; Wang, Xiaohe; Sun, Jie; Jin, Hongguang

2017-01-01

Highlights: • A solar-biomass hybrid power system with zero carbon dioxide emission is proposed. • The internal mechanisms of the solar-biomass utilization are discussed. • The on-design and off-design properties of the system are numerically investigated. • The configurations of the proposed system are optimized. - Abstract: A solar-biomass hybrid power generation system, which integrates a solar thermal energy collection subsystem, a biomass steam boiler and a steam turbine power generation block, is developed for efficiently utilizing renewable energies. The solar thermal energy is concentrated by parabolic trough collectors and is used to heat the feed-water to the superheated steam of 371 °C, then the generated solar steam is further heated to a higher temperature level of 540 °C via a second-stage heating process in a biomass boiler, the system power generation capacity is about 50 MW. The hybrid process of the solar energy and biomass contributes to ameliorating the system thermodynamic performances and reducing of the exergy loss within the steam generation process. The off-design evaluation results indicate that the annual net solar-to-electric efficiency of the hybrid power system is improved to 18.13%, which is higher than that of the typical parabolic trough solar power system as 15.79%. The levelized cost of energy drops to 0.077 $/(kW h) from 0.192 $/(kW h). The annual biomass consumption rate is reduced by 22.53% in comparison with typical biomass power systems. The research findings provide a promising approach for the efficient utilization of the abundant renewable energies resources and the reduction of carbon dioxide emission.

Energy Optimization for a Weak Hybrid Power System of an Automobile Exhaust Thermoelectric Generator

Science.gov (United States)

Fang, Wei; Quan, Shuhai; Xie, Changjun; Tang, Xinfeng; Ran, Bin; Jiao, Yatian

2017-11-01

An integrated starter generator (ISG)-type hybrid electric vehicle (HEV) scheme is proposed based on the automobile exhaust thermoelectric generator (AETEG). An eddy current dynamometer is used to simulate the vehicle's dynamic cycle. A weak ISG hybrid bench test system is constructed to test the 48 V output from the power supply system, which is based on engine exhaust-based heat power generation. The thermoelectric power generation-based system must ultimately be tested when integrated into the ISG weak hybrid mixed power system. The test process is divided into two steps: comprehensive simulation and vehicle-based testing. The system's dynamic process is simulated for both conventional and thermoelectric powers, and the dynamic running process comprises four stages: starting, acceleration, cruising and braking. The quantity of fuel available and battery pack energy, which are used as target vehicle energy functions for comparison with conventional systems, are simplified into a single energy target function, and the battery pack's output current is used as the control variable in the thermoelectric hybrid energy optimization model. The system's optimal battery pack output current function is resolved when its dynamic operating process is considered as part of the hybrid thermoelectric power generation system. In the experiments, the system bench is tested using conventional power and hybrid thermoelectric power for the four dynamic operation stages. The optimal battery pack curve is calculated by functional analysis. In the vehicle, a power control unit is used to control the battery pack's output current and minimize energy consumption. Data analysis shows that the fuel economy of the hybrid power system under European Driving Cycle conditions is improved by 14.7% when compared with conventional systems.

Statistics that learn: can logistic discriminant analysis improve diagnosis in brain SPECT?

International Nuclear Information System (INIS)

Behin-Ain, S.; Barnden, L.; Kwiatek, R.; Del Fante, P.; Casse, R.; Burnet, R.; Chew, G.; Kitchener, M.; Boundy, K.; Unger, S.

2002-01-01

Full text: Logistic discriminant analysis (LDA) is a statistical technique capable of discriminating individuals within a diseased group against normals. It also enables classification of various diseases within a group of patients. This technique provides a quantitative, automated and non-subjective clinical diagnostic tool. Based on a population known to have the disease and a normal control group, an algorithm was developed and trained to identify regions in the human brain responsible for the disease in question. The algorithm outputs a statistical map representing diseased or normal probability on a voxel or cluster basis from which an index is generated for each subject. The algorithm also generates a set of coefficients which is used to generate an index for the purpose of classification of new subjects. The results are comparable and complement those of Statistical Parametric Mapping (SPM) which employs a more common linear discriminant technique. The results are presented for brain SPECT studies of two diseases: chronic fatigue syndrome (CFS) and fibromyalgia (FM). A 100% specificity and 94% sensitivity is achieved for the CFS study (similar to SPM results) and for the FM study 82% specificity and 94% sensitivity is achieved with corresponding SPM results showing 90% specificity and 82% sensitivity. The results encourages application of LDA for discrimination of new single subjects as well as of diseased and normal groups. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Directory of Open Access Journals (Sweden)

Muhammad Sohaib

2017-12-01

Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

Science.gov (United States)

Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

2017-12-11

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

International Nuclear Information System (INIS)

Chen, Jun; Rabiti, Cristian

2017-01-01

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system configuration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. Requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated. - Highlights: • Computational model to synthesize artificial wind speed data with consistent characteristics with database. • Fourier series to capture seasonal trends in the database. • Monte Carlo simulation and probabilistic analysis of hybrid energy systems. • Investigation of the effect of battery in smoothing variability of wind power generation.

Multi-Agent System based Event-Triggered Hybrid Controls for High-Security Hybrid Energy Generation Systems

DEFF Research Database (Denmark)

Dou, Chun-Xia; Yue, Dong; Guerrero, Josep M.

2017-01-01

This paper proposes multi-agent system based event- triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a mul-ti-agent system is constituted by an upper-level central coordi-nated control agent combined with several lower......-level unit agents. Each lower-level unit agent is responsible for dealing with internal switching control and distributed dynamic regula-tion for its unit system. The upper-level agent implements coor-dinated switching control to guarantee the power supply of over-all system with high security. The internal...

Robust Power Management Control for Stand-Alone Hybrid Power Generation System

International Nuclear Information System (INIS)

Kamal, Elkhatib; Adouane, Lounis; Aitouche, Abdel; Mohammed, Walaa

2017-01-01

This paper presents a new robust fuzzy control of energy management strategy for the stand-alone hybrid power systems. It consists of two levels named centralized fuzzy supervisory control which generates the power references for each decentralized robust fuzzy control. Hybrid power systems comprises: a photovoltaic panel and wind turbine as renewable sources, a micro turbine generator and a battery storage system. The proposed control strategy is able to satisfy the load requirements based on a fuzzy supervisor controller and manage power flows between the different energy sources and the storage unit by respecting the state of charge and the variation of wind speed and irradiance. Centralized controller is designed based on If-Then fuzzy rules to manage and optimize the hybrid power system production by generating the reference power for photovoltaic panel and wind turbine. Decentralized controller is based on the Takagi-Sugeno fuzzy model and permits us to stabilize each photovoltaic panel and wind turbine in presence of disturbances and parametric uncertainties and to optimize the tracking reference which is given by the centralized controller level. The sufficient conditions stability are formulated in the format of linear matrix inequalities using the Lyapunov stability theory. The effectiveness of the proposed Strategy is finally demonstrated through a SAHPS (stand-alone hybrid power systems) to illustrate the effectiveness of the overall proposed method. (paper)

Pharmacological evidence that both cognitive memory and habit formation contribute to within-session learning of concurrent visual discriminations.

Science.gov (United States)

Turchi, Janita; Devan, Bryan; Yin, Pingbo; Sigrist, Emmalynn; Mishkin, Mortimer

2010-07-01

The monkey's ability to learn a set of visual discriminations presented concurrently just once a day on successive days (24-h ITI task) is based on habit formation, which is known to rely on a visuo-striatal circuit and to be independent of visuo-rhinal circuits that support one-trial memory. Consistent with this dissociation, we recently reported that performance on the 24-h ITI task is impaired by a striatal-function blocking agent, the dopaminergic antagonist haloperidol, and not by a rhinal-function blocking agent, the muscarinic cholinergic antagonist scopolamine. In the present study, monkeys were trained on a short-ITI form of concurrent visual discrimination learning, one in which a set of stimulus pairs is repeated not only across daily sessions but also several times within each session (in this case, at about 4-min ITIs). Asymptotic discrimination learning rates in the non-drug condition were reduced by half, from approximately 11 trials/pair on the 24-h ITI task to approximately 5 trials/pair on the 4-min ITI task, and this faster learning was impaired by systemic injections of either haloperidol or scopolamine. The results suggest that in the version of concurrent discrimination learning used here, the short ITIs within a session recruit both visuo-rhinal and visuo-striatal circuits, and that the final performance level is driven by both cognitive memory and habit formation working in concert.

Hybrid High-Impact Pedagogies: Integrating Service-Learning with Three Other High-Impact Pedagogies

Science.gov (United States)

Bringle, Robert G.

2017-01-01

This article proposes enhancing student learning through civic engagement by considering the advantages of integrating service-learning with study away, research, and internships and pre-professional courses into first-order, second-order, and third-order hybrid high-impact pedagogies. Service-learning contributes numerous attributes to the other…

Adventure Learning: Theory and Implementation of Hybrid Learning

Science.gov (United States)

Doering, A.

2008-12-01

Adventure Learning (AL), a hybrid distance education approach, provides students and teachers with the opportunity to learn about authentic curricular content areas while interacting with adventurers, students, and content experts at various locations throughout the world within an online learning environment (Doering, 2006). An AL curriculum and online environment provides collaborative community spaces where traditional hierarchical classroom roles are blurred and learning is transformed. AL has most recently become popular in K-12 classrooms nationally and internationally with millions of students participating online. However, in the literature, the term "adventure learning" many times gets confused with phrases such as "virtual fieldtrip" and activities where someone "exploring" is posting photos and text. This type of "adventure learning" is not "Adventure Learning" (AL), but merely a slideshow of their activities. The learning environment may not have any curricular and/or social goals, and if it does, the environment design many times does not support these objectives. AL, on the other hand, is designed so that both teachers and students understand that their online and curriculum activities are in synch and supportive of the curricular goals. In AL environments, there are no disparate activities as the design considers the educational, social, and technological affordances (Kirschner, Strijbos, Kreijns, & Beers, 2004); in other words, the artifacts of the learning environment encourage and support the instructional goals, social interactions, collaborative efforts, and ultimately learning. AL is grounded in two major theoretical approaches to learning - experiential and inquiry-based learning. As Kolb (1984) noted, in experiential learning, a learner creates meaning from direct experiences and reflections. Such is the goal of AL within the classroom. Additionally, AL affords learners a real-time authentic online learning experience concurrently as they

Creating Turbulent Flow Realizations with Generative Adversarial Networks

Science.gov (United States)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

Modeling documents with Generative Adversarial Networks

OpenAIRE

Glover, John

2016-01-01

This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.

Hybrid power system (hydro, solar and wind) for rural electricity generation

International Nuclear Information System (INIS)

Mahinda Kurukulasuriya

2000-01-01

Generation of affordable cheap electric energy for rural development by a hybrid power system (10-50 kW) of hydropower, solar and wind energies on self determining basis and computer application to determine its performance. In this paper the following topics were discussed, design of hybrid power system, its justification and economic analysis, manufacturing and installation of the system. (Author)

Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model

Directory of Open Access Journals (Sweden)

Mojtaba Salehi

2013-03-01

Full Text Available In recent years, the explosion of learning materials in the web-based educational systems has caused difficulty of locating appropriate learning materials to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable materials to learners. Since users express their opinions based on some specific attributes of items, this paper proposes a hybrid recommender system for learning materials based on their attributes to improve the accuracy and quality of recommendation. The presented system has two main modules: explicit attribute-based recommender and implicit attribute-based recommender. In the first module, weights of implicit or latent attributes of materials for learner are considered as chromosomes in genetic algorithm then this algorithm optimizes the weights according to historical rating. Then, recommendation is generated by Nearest Neighborhood Algorithm (NNA using the optimized weight vectors implicit attributes that represent the opinions of learners. In the second, preference matrix (PM is introduced that can model the interests of learner based on explicit attributes of learning materials in a multidimensional information model. Then, a new similarity measure between PMs is introduced and recommendations are generated by NNA. The experimental results show that our proposed method outperforms current algorithms on accuracy measures and can alleviate some problems such as cold-start and sparsity.

Feasibility study of a hybrid plants (photovoltaic–LPG generator system for rural electrification

Directory of Open Access Journals (Sweden)

Adouane Mabrouk

2016-01-01

Full Text Available The present study investigates the possibility of using a stand-alone photovoltaic/LPG (liquid petroleum gas generator hybrid power system for low-cost electricity production which can satisfy the energy load requirements of a typical remote and isolated rural area. In this context, the optimal dimensions to improve the technical and economical performances of the hybrid system are determined according to the load energy requirements. The proposed system's installation and operating costs are simulated using the Hybrid Optimization Model for Electric Renewable (HOMER, the solar radiation and the system components costs as inputs; and then compared with those of other supply options such as diesel generation.

Intelligent Power Management of hybrid Wind/ Fuel Cell/ Energy Storage Power Generation System

OpenAIRE

A. Hajizadeh; F. Hassanzadeh

2013-01-01

This paper presents an intelligent power management strategy for hybrid wind/ fuel cell/ energy storage power generation system. The dynamic models of wind turbine, fuel cell and energy storage have been used for simulation of hybrid power system. In order to design power flow control strategy, a fuzzy logic control has been implemented to manage the power between power sources. The optimal operation of the hybrid power system is a main goal of designing power management strategy. The hybrid ...

Identification of chaotic systems by neural network with hybrid learning algorithm

International Nuclear Information System (INIS)

Pan, S.-T.; Lai, C.-C.

2008-01-01

Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Science.gov (United States)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

Free-piston engine linear generator for hybrid vehicles modeling study

Science.gov (United States)

Callahan, T. J.; Ingram, S. K.

1995-05-01

Development of a free piston engine linear generator was investigated for use as an auxiliary power unit for a hybrid electric vehicle. The main focus of the program was to develop an efficient linear generator concept to convert the piston motion directly into electrical power. Computer modeling techniques were used to evaluate five different designs for linear generators. These designs included permanent magnet generators, reluctance generators, linear DC generators, and two and three-coil induction generators. The efficiency of the linear generator was highly dependent on the design concept. The two-coil induction generator was determined to be the best design, with an efficiency of approximately 90 percent.

Evaluation of hybrid and distance education learning environments in Spain

OpenAIRE

Ferrer-Cascales, Rosario; Walker, Scott L.; Reig-Ferrer, Abilio; Fernández-Pascual, M. Dolores; Albaladejo-Blázquez, Natalia

2011-01-01

This article describes the adaptation and validation of the Distance Education Learning Environments Survey (DELES) for use in investigating the qualities found in distance and hybrid education psycho-social learning environments in Spain. As Europe moves toward post-secondary student mobility, equanimity in access to higher education, and more standardised degree programs across the European Higher Education Area (EHEA) the need for a high quality method for continually assessing the excelle...

Duo: A Human/Wearable Hybrid for Learning About Common Manipulate Objects

National Research Council Canada - National Science Library

Kemp, Charles C

2002-01-01

... with them. Duo is a human/wearable hybrid that is designed to learn about this important domain of human intelligence by interacting with natural manipulable objects in unconstrained environments...

Discrimination aware decision tree learning

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.

2010-01-01

Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact

An analysis of hybrid power generation systems for a residential load

Directory of Open Access Journals (Sweden)

Ceran Bartosz

2017-01-01

Full Text Available This paper presents the results of an energetic and economical analysis of a hybrid power generation system (HPGS which utilises photovoltaic modules, wind turbines, fuel cells and an electrolyzer with hydrogen tank working as the energy storage. The analysis was carried out for three different residential loads, local solar radiation and local wind speed, based on the real measurement values. The analysis shows the optimal solution and the limits of the investment costs required for the system construction. The presented results confirm the effectiveness of the proposed approach, which could be assumed as a very useful tool in the design and analysis of a hybrid power generation system.

Performance analyses of a hybrid geothermal–fossil power generation system using low-enthalpy geothermal resources

International Nuclear Information System (INIS)

Liu, Qiang; Shang, Linlin; Duan, Yuanyuan

2016-01-01

Highlights: • Geothermal energy is used to preheat the feedwater in a coal-fired power unit. • The performance of a hybrid geothermal–fossil power generation system is analyzed. • Models for both parallel and serial geothermal preheating schemes are presented. • Effects of geothermal source temperatures, distances and heat losses are analyzed. • Power increase of the hybrid system over an ORC and tipping distance are discussed. - Abstract: Low-enthalpy geothermal heat can be efficiently utilized for feedwater preheating in coal-fired power plants by replacing some of the high-grade steam that can then be used to generate more power. This study analyzes a hybrid geothermal–fossil power generation system including a supercritical 1000 MW power unit and a geothermal feedwater preheating system. This study models for parallel and serial geothermal preheating schemes and analyzes the thermodynamic performance of the hybrid geothermal–fossil power generation system for various geothermal resource temperatures. The models are used to analyze the effects of the temperature matching between the geothermal water and the feedwater, the heat losses and pumping power during the geothermal water transport and the resource distance and temperature on the power increase to improve the power generation. The serial geothermal preheating (SGP) scheme generally generates more additional power than the parallel geothermal preheating (PGP) scheme for geothermal resource temperatures of 100–130 °C, but the SGP scheme generates slightly less additional power than the PGP scheme when the feedwater is preheated to as high a temperature as possible before entering the deaerator for geothermal resource temperatures higher than 140 °C. The additional power decreases as the geothermal source distance increases since the pipeline pumping power increases and the geothermal water temperature decreases due to heat losses. More than 50% of the power decrease is due to geothermal

Long term effects of aversive reinforcement on colour discrimination learning in free-flying bumblebees.

Directory of Open Access Journals (Sweden)

Miguel A Rodríguez-Gironés

Full Text Available The results of behavioural experiments provide important information about the structure and information-processing abilities of the visual system. Nevertheless, if we want to infer from behavioural data how the visual system operates, it is important to know how different learning protocols affect performance and to devise protocols that minimise noise in the response of experimental subjects. The purpose of this work was to investigate how reinforcement schedule and individual variability affect the learning process in a colour discrimination task. Free-flying bumblebees were trained to discriminate between two perceptually similar colours. The target colour was associated with sucrose solution, and the distractor could be associated with water or quinine solution throughout the experiment, or with one substance during the first half of the experiment and the other during the second half. Both acquisition and final performance of the discrimination task (measured as proportion of correct choices were determined by the choice of reinforcer during the first half of the experiment: regardless of whether bees were trained with water or quinine during the second half of the experiment, bees trained with quinine during the first half learned the task faster and performed better during the whole experiment. Our results confirm that the choice of stimuli used during training affects the rate at which colour discrimination tasks are acquired and show that early contact with a strongly aversive stimulus can be sufficient to maintain high levels of attention during several hours. On the other hand, bees which took more time to decide on which flower to alight were more likely to make correct choices than bees which made fast decisions. This result supports the existence of a trade-off between foraging speed and accuracy, and highlights the importance of measuring choice latencies during behavioural experiments focusing on cognitive abilities.

Discrimination aware decision tree learning

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.

2010-01-01

Recently, the following problem of discrimination aware classification was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact

The benefit of generating errors during learning.

Science.gov (United States)

Potts, Rosalind; Shanks, David R

2014-04-01

Testing has been found to be a powerful learning tool, but educators might be reluctant to make full use of its benefits for fear that any errors made would be harmful to learning. We asked whether testing could be beneficial to memory even during novel learning, when nearly all responses were errors, and where errors were unlikely to be related to either cues or targets. In 4 experiments, participants learned definitions for unfamiliar English words, or translations for foreign vocabulary, by generating a response and being given corrective feedback, by reading the word and its definition or translation, or by selecting from a choice of definitions or translations followed by feedback. In a final test of all words, generating errors followed by feedback led to significantly better memory for the correct definition or translation than either reading or making incorrect choices, suggesting that the benefits of generation are not restricted to correctly generated items. Even when information to be learned is novel, errorful generation may play a powerful role in potentiating encoding of corrective feedback. Experiments 2A, 2B, and 3 revealed, via metacognitive judgments of learning, that participants are strikingly unaware of this benefit, judging errorful generation to be a less effective encoding method than reading or incorrect choosing, when in fact it was better. Predictions reflected participants' subjective experience during learning. If subjective difficulty leads to more effort at encoding, this could at least partly explain the errorful generation advantage.

Generation Z, Meet Cooperative Learning

Science.gov (United States)

Igel, Charles; Urquhart, Vicki

2012-01-01

Today's Generation Z teens need to develop teamwork and social learning skills to be successful in the 21st century workplace. Teachers can help students develop these skills and enhance academic achievement by implementing cooperative learning strategies. Three key principles for successful cooperative learning are discussed. (Contains 1 figure.)

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

Directory of Open Access Journals (Sweden)

Zilong Zhou

2018-01-01

Full Text Available The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM, naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

Science.gov (United States)

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Potency of Thermoelectric Generator for Hybrid Vehicle

Directory of Open Access Journals (Sweden)

Nandy Putra

2010-10-01

Full Text Available Thermoelectric Generator (TEG has been known as electricity generation for many years. If the temperature difference occurred between two difference semi conductor materials, the current will flow in the material and produced difference voltage. This principle is known as Seebeck effect that is the opposite of Peltier effect Thermoelectric Cooling (TEC. This research was conducted to test the potential of electric source from twelve peltier modules. Then, these thermoelectric generators were applied in hybrid car by using waste heat from the combustion engine. The experiment has been conducted with variations of peltier module arrangements (series and parallels and heater as heat source for the thermoelectric generator, with variations of heater voltage input (110V and 220V applied. The experimental result showed that twelve of peltier modules arranged in series and heater voltage of 220V generated power output of 8.11 Watts with average temperature difference of 42.82°C. This result shows that TEG has a bright prospect as alternative electric source.

An Information Analysis of 2-, 3-, and 4-Word Verbal Discrimination Learning.

Science.gov (United States)

Arima, James K.; Gray, Francis D.

Information theory was used to qualify the difficulty of verbal discrimination (VD) learning tasks and to measure VD performance. Words for VD items were selected with high background frequency and equal a priori probabilities of being selected as a first response. Three VD lists containing only 2-, 3-, or 4-word items were created and equated for…

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

Science.gov (United States)

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Visual Hybrid Development Learning System (VHDLS) framework for children with autism.

Science.gov (United States)

Banire, Bilikis; Jomhari, Nazean; Ahmad, Rodina

2015-10-01

The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid Development Learning System (VHDLS) framework that is based on an instructional design model, multimedia cognitive learning theory, and learning style in order to guide software developers in developing learning systems for children with autism. The results from this study showed that the attention of children with autism increased more with the proposed VHDLS framework.

Solution Approach to Automatic Generation Control Problem Using Hybridized Gravitational Search Algorithm Optimized PID and FOPID Controllers

Directory of Open Access Journals (Sweden)

DAHIYA, P.

2015-05-01

Full Text Available This paper presents the application of hybrid opposition based disruption operator in gravitational search algorithm (DOGSA to solve automatic generation control (AGC problem of four area hydro-thermal-gas interconnected power system. The proposed DOGSA approach combines the advantages of opposition based learning which enhances the speed of convergence and disruption operator which has the ability to further explore and exploit the search space of standard gravitational search algorithm (GSA. The addition of these two concepts to GSA increases its flexibility for solving the complex optimization problems. This paper addresses the design and performance analysis of DOGSA based proportional integral derivative (PID and fractional order proportional integral derivative (FOPID controllers for automatic generation control problem. The proposed approaches are demonstrated by comparing the results with the standard GSA, opposition learning based GSA (OGSA and disruption based GSA (DGSA. The sensitivity analysis is also carried out to study the robustness of DOGSA tuned controllers in order to accommodate variations in operating load conditions, tie-line synchronizing coefficient, time constants of governor and turbine. Further, the approaches are extended to a more realistic power system model by considering the physical constraints such as thermal turbine generation rate constraint, speed governor dead band and time delay.

Designing hybrid grass genomes to control runoff generation

Science.gov (United States)

MacLeod, C.; Binley, A.; Humphreys, M.; King, I. P.; O'Donovan, S.; Papadopoulos, A.; Turner, L. B.; Watts, C.; Whalley, W. R.; Haygarth, P.

2010-12-01

Sustainable management of water in landscapes requires balancing demands of agricultural production whilst moderating downstream effects like flooding. Pasture comprises 69% of global agricultural areas and is essential for producing food and fibre alongside environmental goods and services. Thus there is a need to breed forage grasses that deliver multiple benefits through increased levels of productivity whilst moderating fluxes of water. Here we show that a novel grass hybrid that combines the entire genomes of perennial ryegrass (Lolium perenne - the grass of choice for Europe’s forage agriculture) and meadow fescue (Festuca pratensis) has a significant role in flood prevention. Field plot experiments established differences in runoff generation with the hybrid cultivar reducing runoff by 50% compared to perennial ryegrass cultivar, and by 35% compared to a meadow fescue cultivar (34 events over two years, replicated randomized-block design, statistically significant differences). This important research outcome was the result of a project that combined plant genetics, soil physics and plot scale hydrology to identify novel grass genotypes that can reduce runoff from grassland systems. Through a coordinated series of experiments examining effects from the gene to plot scale, we have identified that the rapid growth and then turnover of roots in the L. perenne x F. pratensis hybrid is likely to be a key mechanism in reducing runoff generation. More broadly this is an exciting first step to realizing the potential to design grass genomes to achieve both food production, and to deliver flood control, a key ecosystem service.

The Use of a Hybrid Strategy Combining Problem-based Learning and Magisterial Lectures to Enhance Learning

Directory of Open Access Journals (Sweden)

Carlos Alberto Acosta-Nassar

2014-09-01

Full Text Available This paper addresses the problem of capturing the attention of intermediate level students in the Thermodynamics 1 course from the Mechanical and Agricultural Engineering Program, with the purpose of helping students improve their learning process. A hybrid teaching strategy was proposed based on Problem-based Learning (PBL principles combined with magisterial lectures. Digital and traditional didactic resources were also used in order to find the best mean to minimize the lack of attention in learners. The strategy was developed by sensitizing students to get involved in their formation process. PowerPoint presentations, video clips, the traditional white board and an ultra slim digital tablet board were used to develop the theoretical issues and present the solutions to the problems chosen for the PBL strategy. Finally, the strategy was evaluated and results were analyzed, indicating that using a hybrid strategy combining PBL and traditional magisterial lectures is an optimal resource to improve the learning process of students taking Thermodynamics 1. In addition, it was also concluded that the ultra slim digital tablet board is the optimal didactic resource.

Roles of Approval Motivation and Generalized Expectancy for Reinforcement in Children's Conceptual Discrimination Learning

Science.gov (United States)

Nyce, Peggy A.; And Others

1977-01-01

Forty-four third graders were given a two-choice conceptual discrimination learning task. The two major factors were (1) four treatment groups varying at the extremes on two personality measures, approval motivation and locus of control and (2) sex. (MS)

Dissociable Hippocampal and Amygdalar D1-like receptor contribution to Discriminated Pavlovian conditioned approach learning

Science.gov (United States)

Andrzejewski, Matthew E; Ryals, Curtis

2016-01-01

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

Growth, morphology, and developmental instability of rainbow trout, Yellowstone cutthroat trout, and four hybrid generations

Science.gov (United States)

Ostberg, C.O.; Duda, J.J.; Graham, J.H.; Zhang, S.; Haywood, K. P.; Miller, B.; Lerud, T.L.

2011-01-01

Hybridization of cutthroat trout Oncorhynchus clarkii with nonindigenous rainbow trout O. mykiss contributes to the decline of cutthroat trout subspecies throughout their native range. Introgression by rainbow trout can swamp the gene pools of cutthroat trout populations, especially if there is little selection against hybrids. We used rainbow trout, Yellowstone cutthroat trout O. clarkii bouvieri, and rainbow trout × Yellowstone cutthroat trout F1 hybrids as parents to construct seven different line crosses: F1 hybrids (both reciprocal crosses), F2 hybrids, first-generation backcrosses (both rainbow trout and Yellowstone cutthroat trout), and both parental taxa. We compared growth, morphology, and developmental instability among these seven crosses reared at two different temperatures. Growth was related to the proportion of rainbow trout genome present within the crosses. Meristic traits were influenced by maternal, additive, dominant, overdominant, and (probably) epistatic genetic effects. Developmental stability, however, was not disturbed in F1 hybrids, F2 hybrids, or backcrosses. Backcrosses were morphologically similar to their recurrent parent. The lack of developmental instability in hybrids suggests that there are few genetic incompatibilities preventing introgression. Our findings suggest that hybrids are not equal: that is, growth, development, character traits, and morphology differ depending on the genomic contribution from each parental species as well as the hybrid generation.

A comparative study of leachate quality and biogas generation in simulated anaerobic and hybrid bioreactors

Energy Technology Data Exchange (ETDEWEB)

Xu, Qiyong; Tian, Ying; Wang, Shen; Ko, Jae Hac, E-mail: jaehacko@pkusz.edu.cn

2015-07-15

Highlights: • Temporary aeration shortened the initial acid inhibition phase for methanogens. • COD decreased faster in the hybrid bioreactor than that in the anaerobic control. • Methane generations from hybrid bioreactors were 133.4 L/kg{sub vs} and 113.2 L/kg{sub vs}. • MSW settlement increased with increasing the frequency of intermittent aeration. - Abstract: Research has been conducted to compare leachate characterization and biogas generation in simulated anaerobic and hybrid bioreactor landfills with typical Chinese municipal solid waste (MSW). Three laboratory-scale reactors, an anaerobic (A1) and two hybrid bioreactors (C1 and C2), were constructed and operated for about 10 months. The hybrid bioreactors were operated in an aerobic–anaerobic mode with different aeration frequencies by providing air into the upper layer of waste. Results showed that the temporary aeration into the upper layer aided methane generation by shortening the initial acidogenic phase because of volatile fatty acids (VFAs) reduction and pH increase. Chemical oxygen demand (COD) decreased faster in the hybrid bioreactors, but the concentrations of ammonia–nitrogen in the hybrid bioreactors were greater than those in the anaerobic control. Methanogenic conditions were established within 75 d and 60 d in C1 and C2, respectively. However, high aeration frequency led to the consumption of organic matters by aerobic degradation and resulted in reducing accumulative methane volume. The temporary aeration enhanced waste settlement and the settlement increased with increasing the frequency of aeration. Methane production was inhibited in the anaerobic control; however, the total methane generations from hybrid bioreactors were 133.4 L/kg{sub vs} and 113.2 L/kg{sub vs}. As for MSW with high content of food waste, leachate recirculation right after aeration stopped was not recommended due to VFA inhibition for methanogens.

Dissociable contributions of the orbitofrontal and infralimbic cortex to pavlovian autoshaping and discrimination reversal learning: further evidence for the functional heterogeneity of the rodent frontal cortex.

Science.gov (United States)

Chudasama, Y; Robbins, Trevor W

2003-09-24

To examine possible heterogeneity of function within the ventral regions of the rodent frontal cortex, the present study compared the effects of excitotoxic lesions of the orbitofrontal cortex (OFC) and the infralimbic cortex (ILC) on pavlovian autoshaping and discrimination reversal learning. During the pavlovian autoshaping task, in which rats learn to approach a stimulus predictive of reward [conditional stimulus (CS+)], only the OFC group failed to acquire discriminated approach but was unimpaired when preoperatively trained. In the visual discrimination learning and reversal task, rats were initially required to discriminate a stimulus positively associated with reward. There was no effect of either OFC or ILC lesions on discrimination learning. When the stimulus-reward contingencies were reversed, both groups of animals committed more errors, but only the OFC-lesioned animals were unable to suppress the previously rewarded stimulus-reward association, committing more "stimulus perseverative" errors. In contrast, the ILC group showed a pattern of errors that was more attributable to "learning" than perseveration. These findings suggest two types of dissociation between the effects of OFC and ILC lesions: (1) OFC lesions impaired the learning processes implicated in pavlovian autoshaping but not instrumental simultaneous discrimination learning, whereas ILC lesions were unimpaired at autoshaping and their reversal learning deficit did not reflect perseveration, and (2) OFC lesions induced perseverative responding in reversal learning but did not disinhibit responses to pavlovian CS-. In contrast, the ILC lesion had no effect on response inhibitory control in either of these settings. The findings are discussed in the context of dissociable executive functions in ventral sectors of the rat prefrontal cortex.

Optimum design and research on novel vehicle hybrid excitation synchronous generator

Directory of Open Access Journals (Sweden)

Liu Zhong-Shu

2017-01-01

Full Text Available Hybrid excitation is an organic combination of permanent magnet excitation and electric excitation. Hybrid excitation synchronous generator (HESG both has the advantages of light quality, less losses and high efficiency like permanent magnet generator and the advantages of good magnetic field adjusting performance like electric excitation generator, so it is very suitable for the vehicle application. This paper presented a novel vehicle HESG which has skew stator core, permanent magnet rotor and both armature winding and field winding in the stator. Using ANSYS software, simulating the electric excitation field and the magnetic field, and finally the main parameters of HESG were designed. The simulation and the test results both show that the novel vehicle PMSG has the advantages of small cogging torque, high efficiency, small harmonic component output voltage and low waveform aberration, so as to meet the design requirements fully.

Factor Xa generation by computational modeling: an additional discriminator to thrombin generation evaluation.

Directory of Open Access Journals (Sweden)

Kathleen E Brummel-Ziedins

Full Text Available Factor (fXa is a critical enzyme in blood coagulation that is responsible for the initiation and propagation of thrombin generation. Previously we have shown that analysis of computationally generated thrombin profiles is a tool to investigate hemostasis in various populations. In this study, we evaluate the potential of computationally derived time courses of fXa generation as another approach for investigating thrombotic risk. Utilizing the case (n = 473 and control (n = 426 population from the Leiden Thrombophilia Study and each individual's plasma protein factor composition for fII, fV, fVII, fVIII, fIX, fX, antithrombin and tissue factor pathway inhibitor, tissue factor-initiated total active fXa generation was assessed using a mathematical model. FXa generation was evaluated by the area under the curve (AUC, the maximum rate (MaxR and level (MaxL and the time to reach these, TMaxR and TMaxL, respectively. FXa generation was analyzed in the entire populations and in defined subgroups (by sex, age, body mass index, oral contraceptive use. The maximum rates and levels of fXa generation occur over a 10- to 12- fold range in both cases and controls. This variation is larger than that observed with thrombin (3-6 fold in the same population. The greatest risk association was obtained using either MaxR or MaxL of fXa generation; with an ∼2.2 fold increased risk for individuals exceeding the 90(th percentile. This risk was similar to that of thrombin generation(MaxR OR 2.6. Grouping defined by oral contraceptive (OC use in the control population showed the biggest differences in fXa generation; a >60% increase in the MaxR upon OC use. FXa generation can distinguish between a subset of individuals characterized by overlapping thrombin generation profiles. Analysis of fXa generation is a phenotypic characteristic which may prove to be a more sensitive discriminator than thrombin generation among all individuals.

Intergenerational Learning (Between Generation X & Y) in Learning Families: A Narrative Inquiry

Science.gov (United States)

Ho, C. Y. Cherri

2010-01-01

The purpose of this study is to examine intergenerational learning behaviour within ten Hong Kong families between Generation X parents and their Generation Y children. It tries to investigate intergenerational knowledge exchange, identify the characteristics of learning behaviour and culture in their "learning families". A narrative…

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Science.gov (United States)

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

A triple hybrid micropower generator with simultaneous multi-mode energy harvesting

Science.gov (United States)

Uluşan, H.; Chamanian, S.; Pathirana, W. P. M. R.; Zorlu, Ö.; Muhtaroğlu, A.; Külah, H.

2018-01-01

This study presents a triple hybrid energy harvesting system that combines harvested power from thermoelectric (TE), vibration-based electromagnetic (EM) and piezoelectric (PZT) harvesters into a single DC supply. A power management circuit is designed and implemented in 180 nm standard CMOS technology based on the distinct requirements of each harvester, and is terminated with a Schottky diode to avoid reverse current flow. The system topology hence supports simultaneous power generation and delivery from low and high frequency vibrations as well as temperature differences in the environment. The ultra-low DC voltage harvested from TE generator is boosted with a cross-coupled charge-pump driven by an LC oscillator with fully-integrated center-tapped differential inductors. The EM harvester output was rectified with a self-powered and low drop-out AC/DC doubler circuit. The PZT interface electronics benefits from peak-to-peak cycle of the harvested voltage through a negative voltage converter followed by synchronous power extraction and DC-to-DC conversion through internal switches, and an external inductor. The hybrid system was tested with a wearable in-house EM energy harvester placed wrist of a jogger, a commercial low volume PZT harvester, and DC supply as the TE generator output. The system generates more than 1.2 V output for load resistances higher than 50 kΩ, which corresponds to 24 μW to power wearable sensors. Simultaneous multi-mode operation achieves higher voltage and power compared to stand-alone harvesting circuits, and generates up to 110 μW of output power. This is the first hybrid harvester circuit that simultaneously extracts energy from three independent sources, and delivers a single DC output.

Enhanced discriminative fear learning of phobia-irrelevant stimuli in spider-fearful individuals

Directory of Open Access Journals (Sweden)

Carina eMosig

2014-10-01

Full Text Available Avoidance is considered as a central hallmark of all anxiety disorders. The acquisition and expression of avoidance which leads to the maintenance and exacerbation of pathological fear is closely linked to Pavlovian and operant conditioning processes. Changes in conditionability might represent a key feature of all anxiety disorders but the exact nature of these alterations might vary across different disorders. To date, no information is available on specific changes in conditionability for disorder-irrelevant stimuli in specific phobia (SP. The first aim of this study was to investigate changes in fear acquisition and extinction in spider-fearful individuals as compared to non-fearful participants by using the de novo fear conditioning paradigm. Secondly, we aimed to determine whether differences in the magnitude of context-dependent fear retrieval exist between spider-fearful and non-fearful individuals. Our findings point to an enhanced fear discrimination in spider-fearful individuals as compared to non-fearful individuals at both the physiological and subjective level. The enhanced fear discrimination in spider-fearful individuals was neither mediated by increased state anxiety, depression, nor stress tension. Spider-fearful individuals displayed no changes in extinction learning and/or fear retrieval. Surprisingly, we found no evidence for context-dependent modulation of fear retrieval in either group. Here we provide first evidence that spider-fearful individuals show an enhanced discriminative fear learning of phobia-irrelevant (de novo stimuli. Our findings provide novel insights into the role of fear acquisition and expression for the development and maintenance of maladaptive responses in the course of SP.

The Effect of Think-Pair-Share-Write Based on Hybrid Learning on Metakognitive Skills, Creative Thinking and Cognitive Learning at SMA Negeri 3 Malang

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Ika Yulianti Siregar

2017-07-01

Full Text Available The results of biology learning observation show that there are many constraints during the learning process in the class and consultation meeting between teacher and students. The think-pair-share-write based on hybrid learning was conducted to analyze the effect on metacognitive skills, creative thinking and learning outcomes. The research design was quasi experiment with pretest-posttest non-equivalent control group design. The independent variable is think-pair-share-write based on Hybrid learning model, while the dependent variables are metacognitive skills, creative thinking, and cognitive learning outcomes. Metacognitive skills are measured by using metacognitive rubrics. Creative thinking skills and cognitive learning outcomes are measured by using a description test. The data were taken by conducting pretest and posttest. The hypothesis test used was anakova with level of significance 0,05 (P <0,05, as the test result was significant then the test was continued to LSD. Before the anakova test, normality and homogeneity test were performed. The results showed that think-pair-share-write based on Hybrid Learning significantly affecting: 1 the metacognitive skills with F arithmetic of 183,472 and Sig. 0,000; 2 the creative thinking skill with F value of 325,111 and Sig. 0,000; 3 the cognitive learning outcomes with F arithmetic of 175.068 and Sig. 0,000.

Semantic e-Learning: Next Generation of e-Learning?

Science.gov (United States)

Konstantinos, Markellos; Penelope, Markellou; Giannis, Koutsonikos; Aglaia, Liopa-Tsakalidi

Semantic e-learning aspires to be the next generation of e-learning, since the understanding of learning materials and knowledge semantics allows their advanced representation, manipulation, sharing, exchange and reuse and ultimately promote efficient online experiences for users. In this context, the paper firstly explores some fundamental Semantic Web technologies and then discusses current and potential applications of these technologies in e-learning domain, namely, Semantic portals, Semantic search, personalization, recommendation systems, social software and Web 2.0 tools. Finally, it highlights future research directions and open issues of the field.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

Science.gov (United States)

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

Directory of Open Access Journals (Sweden)

Zehui Kong

Full Text Available To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM of power-request is derived. The reinforcement learning (RL is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

Science.gov (United States)

Yu, Jianbo

2016-11-01

The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

Photovoltaic-wind hybrid autonomous generation systems in Mongolia

Energy Technology Data Exchange (ETDEWEB)

Dei, Tsutomu; Ushiyama, Izumi

2005-01-01

Two hybrid stand-alone (autonomous) power systems, each with wind and PV generation, were studied as installed at health clinics in semi-desert and mountainous region in Mongolia. Meteorological and system operation parameters, including power output and the consumption of the system, were generally monitored by sophisticated monitoring. However, where wind and solar site information was lacking, justifiable estimates were made. The results show that there is a seasonal complementary relationship between wind and solar irradiation in Tarot Sum. The users understood the necessity of Demand Side Management of isolated wind-PV generation system through technology transfer seminars and actually executed DSM at both sites. (author)

Transfer of Perceptual Learning of Depth Discrimination Between Local and Global Stereograms

OpenAIRE

Gantz, Liat; Bedell, Harold

2010-01-01

Several previous studies reported differences when stereothresholds are assessed with local-contour stereograms vs. complex random-dot stereograms (RDSs). Dissimilar thresholds may be due to differences in the properties of the stereograms (e.g., spatial frequency content, contrast, inter-element separation, area) or to different underlying processing mechanisms. This study examined the transfer of perceptual learning of depth discrimination between local and global RDSs with similar properti...

Modeling, design and analysis of a stand-alone hybrid power generation system using solar/urine

International Nuclear Information System (INIS)

Wu, Wei; Zhou, Ya-Yan; Lin, Mu-Hsuan; Hwang, Jenn-Jiang

2013-01-01

Highlights: • The stand-alone hybrid power system is presented. • The urine-to-hydrogen processor is proposed. • Scenario analysis of the hybrid power dispatching and the urine/solar demands is investigated. • The design, modeling and optimization of the hybrid power system is addressed by Aspen Plus and Matlab. - Abstract: The urine turned to hydrogen as an energy conversion process is integrated into a stand-alone hybrid (PV/FC/battery) power generation system. The optimization and simulation of a new urine-to-hydrogen processor is evaluated in Aspen Plus environment. In our approach, the PV generator aims to reduce urine consumption and the lithium-ion battery can compensate the power gap due to the fuel processing delay. Based on prescribed patterns of solar irradiation and the daily load demand of a 30-persons classroom, scenario analyses of the hybrid power dispatching and operational feasibility is addressed

Maze learning by a hybrid brain-computer system.

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-13

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Maze learning by a hybrid brain-computer system

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-01

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Hybrid mesh generation for the new generation of oil reservoir simulators: 3D extension; Generation de maillage hybride pour les simulateurs de reservoir petrolier de nouvelle generation: extension 3D

Energy Technology Data Exchange (ETDEWEB)

Flandrin, N.

2005-09-15

During the exploitation of an oil reservoir, it is important to predict the recovery of hydrocarbons and to optimize its production. A better comprehension of the physical phenomena requires to simulate 3D multiphase flows in increasingly complex geological structures. In this thesis, we are interested in this spatial discretization and we propose to extend in 3D the 2D hybrid model proposed by IFP in 1998 that allows to take directly into account in the geometry the radial characteristics of the flows. In these hybrid meshes, the wells and their drainage areas are described by structured radial circular meshes and the reservoirs are represented by structured meshes that can be a non uniform Cartesian grid or a Corner Point Geometry grids. In order to generate a global conforming mesh, unstructured transition meshes based on power diagrams and satisfying finite volume properties are used to connect the structured meshes together. Two methods have been implemented to generate these transition meshes: the first one is based on a Delaunay triangulation, the other one uses a frontal approach. Finally, some criteria are introduced to measure the quality of the transition meshes and optimization procedures are proposed to increase this quality under finite volume properties constraints. (author)

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

Science.gov (United States)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Generation of New Genotypic and Phenotypic Features in Artificial and Natural Yeast Hybrids

Directory of Open Access Journals (Sweden)

Walter P. Pfliegler

2014-01-01

Full Text Available Evolution and genome stabilization have mostly been studied on the Saccharomyces hybrids isolated from natural and alcoholic fermentation environments. Genetic and phenotypic properties have usually been compared to the laboratory and reference strains, as the true ancestors of the natural hybrid yeasts are unknown. In this way the exact impact of different parental fractions on the genome organization or metabolic activity of the hybrid yeasts is difficult to resolve completely. In the present work the evolution of geno- and phenotypic properties is studied in the interspecies hybrids created by the cross-breeding of S. cerevisiae with S. uvarum or S. kudriavzevii auxotrophic mutants. We hypothesized that the extent of genomic alterations in S. cerevisiae × S. uvarum and S. cerevisiae × S. kudriavzevii should affect the physiology of their F1 offspring in different ways. Our results, obtained by amplified fragment length polymorphism (AFLP genotyping and karyotyping analyses, showed that both subgenomes of the S. cerevisiae x S. uvarum and of S. cerevisiae × S. kudriavzevii hybrids experienced various modifications. However, the S. cerevisiae × S. kudriavzevii F1 hybrids underwent more severe genomic alterations than the S. cerevisiae × S. uvarum ones. Generation of the new genotypes also influenced the physiological performances of the hybrids and the occurrence of novel phenotypes. Significant differences in carbohydrate utilization and distinct growth dynamics at increasing concentrations of sodium chloride, urea and miconazole were observed within and between the S. cerevisiae × S. uvarum and S. cerevisiae × S. kudriavzevii hybrids. Parental strains also demonstrated different contributions to the final metabolic outcomes of the hybrid yeasts. A comparison of the genotypic properties of the artificial hybrids with several hybrid isolates from the wine-related environments and wastewater demonstrated a greater genetic variability of

Fiscal 2000 report on the international joint verification of photovoltaic power generation system. Verification of hybrid system comprising photovoltaic power generation system and micro-hydroelectric power generation systems; 2000 nendo taiyoko hatsuden system kokusai kyodo jissho kaihatsu hokokusho. Taiyoko micro suiryoku hybrid system jissho kenkyu

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-06-01

Research was conducted in Vietnam for the development of a hybrid system comprising a photovoltaic power generation system and a micro-hydroelectric power generation system. In verification test operation, data measurement had been under way for approximately 18 months since it was started in September 1999. The rate of days on which effective data were obtained throughout this period was 93.4%. Power generated by the micro-hydroelectric power generation system was 19.4kWh/d with so small a capacity factor of 3.2%. The capacity factor of the photovoltaic power generation system was again very small at 4.5% since the amount consumed by the load was as small as 131.0kWh/d. Weather data of solar radiation and precipitation were being collected smoothly. In the study of hybrid system optimization, the effect of inductor generator activation upon the inverter was taken up. In the study of capacity balance optimization between the constituent elements of the hybrid system, methodology was established and verified, and calculations were carried out. (NEDO)

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Science.gov (United States)

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Science.gov (United States)

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

International Nuclear Information System (INIS)

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Energy Technology Data Exchange (ETDEWEB)

Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-07-15

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

Electric Motor-Generator for a Hybrid Electric Vehicle

OpenAIRE

Odvářka, Erik; Mebarki, Abdeslam; Gerada, David; Brown, Neil; Ondrůšek, Čestmír

2009-01-01

Several topologies of electrical machines can be used to meet requirements for application in a hybrid electric vehicle. This paper describes process of an electric motor-generator selection, considering electromagnetic, thermal and basic control design. The requested electrical machine must develop 45 kW in continuous operation at 1300 rpm with field weakening capability up to 2500 rpm. Both radial and axial flux topologies are considered as potential candidates. A family of axial flux machi...

Fγ: A new observable for photon-hadron discrimination in hybrid air shower events

Science.gov (United States)

Niechciol, M.; Risse, M.; Ruehl, P.; Settimo, M.; Younk, P. W.; Yushkov, A.

2018-01-01

To search for ultra-high-energy photons in primary cosmic rays, air shower observables are needed that allow a good separation between primary photons and primary hadrons. We present a new observable, Fγ, which can be extracted from ground-array data in hybrid events, where simultaneous measurements of the longitudinal and the lateral shower profile are performed. The observable is based on a template fit to the lateral distribution measured by the ground array with the template taking into account the complementary information from the measurement of the longitudinal profile, i.e. the primary energy and the geometry of the shower. Fγ shows a very good photon-hadron separation, which is even superior to the separation given by the well-known Xmax observable (the atmospheric depth of the shower maximum). At energies around 1 EeV (10 EeV), Fγ provides a background rejection better than 97.8 % (99.9 %) at a signal efficiency of 50 %. Advantages of the observable Fγ are its technical stability with respect to irregularities in the ground array (i.e. missing or temporarily non-operating stations) and that it can be applied over the full energy range accessible to the air shower detector, down to its threshold energy. Furthermore, Fγ complements nicely to Xmax such that both observables can well be combined to achieve an even better discrimination power, exploiting the rich information available in hybrid events.

Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

OpenAIRE

Tong, Tong; Wolz, Robin; Coupe, Pierrick; Hajnal, Joseph V.; Rueckert, Daniel

2013-01-01

International audience; We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labe...

Hierarchical energy management system for stand-alone hybrid system based on generation costs and cascade control

International Nuclear Information System (INIS)

Torreglosa, J.P.; García, P.; Fernández, L.M.; Jurado, F.

2014-01-01

Highlights: • We present an energy management system for a stand-alone WT/PV/hydrogen/battery hybrid system. • Hierarchical control composed by master and slave control strategies. • Control assures reliable electricity support for stand-alone applications subject to technical and economic criteria. - Abstract: This paper presents an energy management system (EMS) for stand-alone hybrid systems composed by photovoltaic (PV) solar panels and a wind turbine (WT) as primary energy sources and two energy storage systems, which are a hydrogen system and a battery. The hydrogen system is composed of fuel cell (FC), electrolyzer and hydrogen storage tank. The EMS is a hierarchical control composed by a master control strategy and a slave control strategy. On the one hand, the master control generates the reference powers to meet several premises (such as to satisfy the load power demand, and to maintain the hydrogen tank level and the state of charge (SOC) of the battery between their target margins), taking also into account economic aspects to discriminate between using the battery or hydrogen system. On the other hand, the slave control modifies the reference powers generated by the master control according to the energy sources dynamic limitations, and maintains the DC bus voltage at its reference value. The models, implemented in MATLAB-Simulink environment, have been developed from commercially available components. To check the viability of the proposed EMS, two kinds of simulations were carried out: (1) A long-term simulation of 25 years (expected lifetime of the system) with a sample time of one hour to validate the master control of the EMS; and (2) A short-term simulation with sudden net power variations to validate the slave control of the EMS

Performance assessment of hybrid power generation systems: Economic and environmental impacts

International Nuclear Information System (INIS)

Al-Sharafi, Abdullah; Yilbas, Bekir S.; Sahin, Ahmet Z.; Ayar, T.

2017-01-01

Highlights: • A double-step optimization tool for hybrid power generation systems is introduced. • Economical aspects and the impact of the system on the environment are considered. • A hybrid system comprises PV array-wind turbine-battery-diesel engine is considered. • Real time analysis of the system for full year simulation is carried out. • System optimum configuration at point where total performance index is maximized. - Abstract: This article aims to introduce a double-step performance assessment tool for the hybrid power generation systems. As a case study, a hybrid system comprising PV array, wind-turbine, battery bank and diesel engine is incorporated in hourly based simulations to meet power demand of a residence unit at Dhahran area, Kingdom of Saudi Arabia. Different indicators related to economical and environmental performance assessments of the hybrid system have been considered. In the economic related assessment case, cost of electricity, energy excess percentage, and operating life cycle indicators have been considered and combined to develop the first overall performance index. Renewable contribution, renewable source availability and environmental impact indicators have been considered for the environmental assessment case and they are combined in the second performance index. For either economical or environmental cases, the optimum configuration of the system is achieved by maximizing the first and second overall performance indicators. This innovative optimization tools gives the designer the freedom to assign suitable weights associated with economical aspect, environmental impact, governmental regulations and social impact, for the first and second overall performance indicators, and combine them in the total performance index. The optimum system configuration is at the point where the total performance index is maximized.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

Science.gov (United States)

Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

The Effect of Haptic Guidance on Learning a Hybrid Rhythmic-Discrete Motor Task.

Science.gov (United States)

Marchal-Crespo, Laura; Bannwart, Mathias; Riener, Robert; Vallery, Heike

2015-01-01

Bouncing a ball with a racket is a hybrid rhythmic-discrete motor task, combining continuous rhythmic racket movements with discrete impact events. Rhythmicity is exceptionally important in motor learning, because it underlies fundamental movements such as walking. Studies suggested that rhythmic and discrete movements are governed by different control mechanisms at different levels of the Central Nervous System. The aim of this study is to evaluate the effect of fixed/fading haptic guidance on learning to bounce a ball to a desired apex in virtual reality with varying gravity. Changing gravity changes dominance of rhythmic versus discrete control: The higher the value of gravity, the more rhythmic the task; lower values reduce the bouncing frequency and increase dwell times, eventually leading to a repetitive discrete task that requires initiation and termination, resembling target-oriented reaching. Although motor learning in the ball-bouncing task with varying gravity has been studied, the effect of haptic guidance on learning such a hybrid rhythmic-discrete motor task has not been addressed. We performed an experiment with thirty healthy subjects and found that the most effective training condition depended on the degree of rhythmicity: Haptic guidance seems to hamper learning of continuous rhythmic tasks, but it seems to promote learning for repetitive tasks that resemble discrete movements.

Problem and Project Based Learning in Hybrid Spaces:Nomads and Artisans

OpenAIRE

Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

2016-01-01

There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how st...

Active semi-supervised learning method with hybrid deep belief networks.

Science.gov (United States)

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Pigeons learn stimulus identity and stimulus relations when both serve as redundant, relevant cues during same-different discrimination training.

Science.gov (United States)

Gibson, Brett M; Wasserman, Edward A

2003-01-01

The authors taught pigeons to discriminate displays of 16 identical items from displays of 16 nonidentical items. Unlike most same-different discrimination studies--where only stimulus relations could serve a discriminative function--both the identity of the items and the relations among the items were discriminative features of the displays. The pigeons learned about both stimulus identity and stimulus relations when these 2 sources of information served as redundant, relevant cues. In tests of associative competition, identity cues exerted greater stimulus control than relational cues. These results suggest that the pigeon can respond to both specific stimuli and general relations in the environment.

Theory of enhanced second-harmonic generation by the quadrupole-dipole hybrid exciton

International Nuclear Information System (INIS)

Roslyak, Oleksiy; Birman, Joseph L

2008-01-01

We report calculated substantial enhancement of the second-harmonic generation (SHG) in cuprous oxide crystals, resonantly hybridized with an appropriate organic material (DCM2:CA:PS 'solid state solvent'). The quadrupole origin of the inorganic part of the quadrupole-dipole hybrid provides inversion symmetry breaking and the organic part contributes to the oscillator strength of the hybrid. We show that the enhancement of the SHG, compared to the bulk cuprous oxide crystal, is proportional to the ratio of the DCM2 dipole moment and the effective dipole moment of the quadrupole transitions in the cuprous oxide. It is also inversely proportional to the line-width of the hybrid and bulk excitons. The enhancement may be regulated by adjusting the organic blend (mutual concentration of the DCM2 and CA part of the solvent) and pumping conditions (varying the angle of incidence in the case of optical pumping or populating the minimum of the lower branch of the hybrid in the case of electrical pumping)

Hybrid electrical generation system utilizing wind, diesel and hydropower for operation of an underground zinc mine in southern Chile

Energy Technology Data Exchange (ETDEWEB)

Gridley, Norman [Minera El Toqui (Chile); Banto, Marcelo [Seawind Chile (Chile)

2010-07-01

This paper presents a hybrid electrical generation system used for underground zinc mine operations that utilizes wind, diesel and hydropower. This mine is located in Coyhaique and had a total energy consumption of 32,567 MWh in 2010 which is anticipated to increase by 25% in 2011. Power generation in this mine is independent of the power grid. It consists of four main portals: ventilation, electrical and drainage systems and ramp access to all mining zones. The technical details for all the parts of the mine and the hybrid generation system are given. A tabular form shows the energy consumed every month from 2005-2010 for all three systems involved, namely wind power generation, diesel generation and the hydro generation system. Benefits of this hybrid system include stability and constant power generation under variable loads. This system can also be applied to other mines using a grid. From the study it can be concluded that the hybrid system is environmentally friendly, economical and sustainable.

Wavelet Based Protection Scheme for Multi Terminal Transmission System with PV and Wind Generation

Science.gov (United States)

Manju Sree, Y.; Goli, Ravi kumar; Ramaiah, V.

2017-08-01

A hybrid generation is a part of large power system in which number of sources usually attached to a power electronic converter and loads are clustered can operate independent of the main power system. The protection scheme is crucial against faults based on traditional over current protection since there are adequate problems due to fault currents in the mode of operation. This paper adopts a new approach for detection, discrimination of the faults for multi terminal transmission line protection in presence of hybrid generation. Transient current based protection scheme is developed with discrete wavelet transform. Fault indices of all phase currents at all terminals are obtained by analyzing the detail coefficients of current signals using bior 1.5 mother wavelet. This scheme is tested for different types of faults and is found effective for detection and discrimination of fault with various fault inception angle and fault impedance.

Data mining for isotope discrimination in atom probe tomography

Energy Technology Data Exchange (ETDEWEB)

Broderick, Scott R. [Department of Materials Science and Engineering and Institute for Combinatorial Discovery, Iowa State University, Ames, IA 50011-2230 (United States); Bryden, Aaron [Ames National Laboratory, Ames, IA 50011-2230 (United States); Suram, Santosh K. [Department of Materials Science and Engineering and Institute for Combinatorial Discovery, Iowa State University, Ames, IA 50011-2230 (United States); Rajan, Krishna, E-mail: krajan@iastate.edu [Department of Materials Science and Engineering and Institute for Combinatorial Discovery, Iowa State University, Ames, IA 50011-2230 (United States)

2013-09-15

Ions with similar time-of-flights (TOF) can be discriminated by mapping their kinetic energy. While current generation position-sensitive detectors have been considered insufficient for capturing the isotope kinetic energy, we demonstrate in this paper that statistical learning methodologies can be used to capture the kinetic energy from all of the parameters currently measured by mathematically transforming the signal. This approach works because the kinetic energy is sufficiently described by the descriptors on the potential, the material, and the evaporation process within atom probe tomography (APT). We discriminate the isotopes for Mg and Al by capturing the kinetic energy, and then decompose the TOF spectrum into its isotope components and identify the isotope for each individual atom measured. This work demonstrates the value of advanced data mining methods to help enhance the information resolution of the atom probe. - Highlights: ► Atom probe tomography and statistical learning were combined for data enhancement. ► Multiple eigenvalue decompositions decomposed a spectrum with overlapping peaks. ► The isotope of each atom was determined by kinetic energy discrimination. ► Eigenspectra were identified and new chemical information was identified.

Does Fine Color Discrimination Learning in Free-Flying Honeybees Change Mushroom-Body Calyx Neuroarchitecture?

Science.gov (United States)

Sommerlandt, Frank M J; Spaethe, Johannes; Rössler, Wolfgang; Dyer, Adrian G

2016-01-01

Honeybees learn color information of rewarding flowers and recall these memories in future decisions. For fine color discrimination, bees require differential conditioning with a concurrent presentation of target and distractor stimuli to form a long-term memory. Here we investigated whether the long-term storage of color information shapes the neural network of microglomeruli in the mushroom body calyces and if this depends on the type of conditioning. Free-flying honeybees were individually trained to a pair of perceptually similar colors in either absolute conditioning towards one of the colors or in differential conditioning with both colors. Subsequently, bees of either conditioning groups were tested in non-rewarded discrimination tests with the two colors. Only bees trained with differential conditioning preferred the previously learned color, whereas bees of the absolute conditioning group, and a stimuli-naïve group, chose randomly among color stimuli. All bees were then kept individually for three days in the dark to allow for complete long-term memory formation. Whole-mount immunostaining was subsequently used to quantify variation of microglomeruli number and density in the mushroom-body lip and collar. We found no significant differences among groups in neuropil volumes and total microglomeruli numbers, but learning performance was negatively correlated with microglomeruli density in the absolute conditioning group. Based on these findings we aim to promote future research approaches combining behaviorally relevant color learning tests in honeybees under free-flight conditions with neuroimaging analysis; we also discuss possible limitations of this approach.

A Hybrid Column Generation approach for an Industrial Waste Collection Routing Problem

DEFF Research Database (Denmark)

Hauge, Kristian; Larsen, Jesper; Lusby, Richard Martin

2014-01-01

, while empty containers must be returned to the depot to await further assignments. Unlike, the traditional ROROR problem, where vehicles may transport one skip container at a time regardless of whether it is full or not, we consider cases in which a vehicle can transport up to eight containers, at most...... two of which can be full. We propose a Generalized Set Partitioning formulation of the problem and describe a hybrid column generation procedure to solve it. A fast Tabu Search heuristic is used to generate new columns. The proposed methodology is tested on nine data sets, four of which are actual......, real-world problem instances. Results indicate that the hybrid column generation outperforms a purely heuristic approach in terms of both running time and solution quality. High quality solutions to problems containing up to 100 orders can be solved in approximately 15 minutes....

Behavior of hybrid concentrated photovoltaic-thermoelectric generator under variable solar radiation

DEFF Research Database (Denmark)

Mahmoudi Nezhad, Sajjad; Rezaniakolaei, Alireza; Rosendahl, Lasse Aistrup

2018-01-01

diversely versus changing the solar radiation and module temperature. Moreover, the thermal response of the TEG stabilizes temperature fluctuation of the hybrid module when the solar radiation rapidly changes. In this work, impact of the thermal contact resistance on the temperature profile and system...... and solved by finite volume algorithm. In spite of temperatures profile in the hybrid CPV-TEG module, as results of variation of solar irradiation, power generation and efficiency of the CPV and TEG under the transient condition are presented. The results show that efficiency of the TEG and CPV varies...

Learning Auditory Discrimination with Computer-Assisted Instruction: A Comparison of Two Different Performance Objectives.

Science.gov (United States)

Steinhaus, Kurt A.

A 12-week study of two groups of 14 college freshmen music majors was conducted to determine which group demonstrated greater achievement in learning auditory discrimination using computer-assisted instruction (CAI). The method employed was a pre-/post-test experimental design using subjects randomly assigned to a control group or an experimental…

Design and development of hybrid energy generator (photovoltaics) with solar tracker

Science.gov (United States)

Mohiuddin, A. K. M.; Sabarudin, Mohamad Syabil Bin; Khan, Ahsan Ali; Izan Ihsan, Sany

2017-03-01

This paper is the outcome of a small scale hybrid energy generator (hydro and photovoltaic) project. It contains the photovoltaics part of the project. The demand of energy resources is increasing day by day. That is why people nowadays tend to move on and changes their energy usage from using fossil fuels to a cleaner and green energy like hydro energy, solar energy etc. Nevertheless, energy is hard to come by for people who live in remote areas and also campsites in the remote areas which need continuous energy sources to power the facilities. Thus, the purpose of this project is to design and develop a small scale hybrid energy generator to help people that are in need of power. This main objective of this project is to develop and analyze the effectiveness of solar trackers in order to increase the electricity generation from solar energy. Software like Solidworks and Arduino is used to sketch and construct the design and also to program the microcontroller respectively. Experimental results show the effectiveness of the designed solar tracker sytem.

Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators

Directory of Open Access Journals (Sweden)

Chouaib Ammari

2017-11-01

Full Text Available In this paper, we will size an optimum hybrid central content three different generators, two on renewable energy (solar photovoltaic and wind power and two nonrenewable (diesel generator and storage system because the new central generator has started to consider the green power technology in order for best future to the world, this central will use all the green power resource available and distributes energy to a small isolated village in southwest of Algeria named “Timiaouine”. The consumption of this village estimated with detailed in two season; season low consumption (winter and high consumption (summer, the hybrid central will be optimized by program Hybrid Optimization Model for Electric Renewable (HOMER PRO, this program will simulate in two configuration, the first with storage system, the second without storage system and in the end the program HOMER PRO will choose the best configuration which is the mixture of both economic and ecologic configurations, this central warrants the energetic continuity of village. Article History: Received May 18th 2017; Received in revised form July 17th 2017; Accepted Sept 3rd 2017; Available online How to Cite This Article: Ammari, C., Hamouda,M., and Makhloufi,S. (2017 Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators. International Journal of Renewable Energy Development, 6(3, 263-272. http://doi.org/10.14710/ijred.6.3.263-272

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

Directory of Open Access Journals (Sweden)

Yi Sun

2017-12-01

Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

Science.gov (United States)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Wind Generator & Biomass No-draft Gasification Hybrid

Science.gov (United States)

Hein, Matthew R.

The premise of this research is that underutilized but vast intermittent renewable energy resources, such as wind, can become more market competitive by coupling with storable renewable energy sources, like biomass; thereby creating a firm capacity resource. Specifically, the Midwest state of South Dakota has immense wind energy potential that is not used because of economic and logistic barriers of electrical transmission or storage. Coupling the state's intermittent wind resource with another of the state's energy resources, cellulosic non-food biomass, by using a wind generator and no-draft biomass gasification hybrid system will result in a energy source that is both firm and storable. The average energy content of common biomass feedstock was determined, 14.8 MJ/kg (7.153 Btu/lb), along with the assumed typical biomass conversion efficiency of the no-draft gasifier, 65%, so that an average electrical energy round trip efficiency (RTE) of 214% can be expected (i.e. One unit of wind electrical energy can produce 2.14 kWh of electrical energy stored as syngas.) from a wind generator and no-draft biomass gasification system. Wind characteristics are site specific so this analysis utilizes a synthetic wind resource to represent a statistically sound gross representation of South Dakota's wind regime based on data from the Wind Resource Assessment Network (WRAN) locations. A synthetic wind turbine generated from common wind turbine power curves and scaled to 1-MW rated capacity was utilized for this analysis in order to remove equipment bias from the results. A standard 8,760-hour BIN Analysis model was constructed within HOMER, powerful simulation software developed by the National Renewable Energy Laboratory (NREL) to model the performance of renewable power systems. It was found that the optimum configuration on a per-megawatt-transmitted basis required a wind generator (wind farm) rated capacity of 3-MW with an anticipated annual biomass feedstock of 26,132 GJ

Three-dimensional pseudo-random number generator for implementing in hybrid computer systems

International Nuclear Information System (INIS)

Ivanov, M.A.; Vasil'ev, N.P.; Voronin, A.V.; Kravtsov, M.Yu.; Maksutov, A.A.; Spiridonov, A.A.; Khudyakova, V.I.; Chugunkov, I.V.

2012-01-01

The algorithm for generating pseudo-random numbers oriented to implementation by using hybrid computer systems is considered. The proposed solution is characterized by a high degree of parallel computing [ru

Not All Same-Different Discriminations Are Created Equal: Evidence Contrary to a Unidimensional Account of Same-Different Learning

Science.gov (United States)

Gibson, Brett M.; Wasserman, Edward A.; Cook, Robert G.

2006-01-01

In Experiment 1, we trained four pigeons to concurrently discriminate displays of 16 same icons (16S) from displays of 16 different icons (16D) as well as between displays of same icons (16S) from displays that contained 15 same icons and one different icon (15S:1D). The birds rapidly learned to discriminate 16S vs. 16D displays, but they failed…

Harmonic Resonance Damping with a Hybrid Compensation System in Power Systems with Dispersed Generation

DEFF Research Database (Denmark)

Chen, Zhe; Pedersen, John Kim; Blaabjerg, Frede

2004-01-01

A hybrid compensation system consisting of an active filter and a group of distributed passive filters has been studied previously. The passive filters are used for each distorting load or Dispersed Generation (DG) unit to remove major harmonics and provide reactive power compensation. The active...... filter is connected in parallel with the distributed passive filters and loads/DGs to correct the system unbalance and remove the remaining harmonic components. The effectiveness of the presented compensation system has also been demonstrated. This paper studies the performance of the hybrid compensation...... demonstrated that the harmonic resonance can be damped effectively. The hybrid filter system is an effective compensation system for dispersed generation systems. In the compensation system, the passive filters are mainly responsible for main harmonic and reactive power compensation of each individual load/ DG...

Generator voltage stabilisation for series-hybrid electric vehicles.

Science.gov (United States)

Stewart, P; Gladwin, D; Stewart, J; Cowley, R

2008-04-01

This paper presents a controller for use in speed control of an internal combustion engine for series-hybrid electric vehicle applications. Particular reference is made to the stability of the rectified DC link voltage under load disturbance. In the system under consideration, the primary power source is a four-cylinder normally aspirated gasoline internal combustion engine, which is mechanically coupled to a three-phase permanent magnet AC generator. The generated AC voltage is subsequently rectified to supply a lead-acid battery, and permanent magnet traction motors via three-phase full bridge power electronic inverters. Two complementary performance objectives exist. Firstly to maintain the internal combustion engine at its optimal operating point, and secondly to supply a stable 42 V supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the internal combustion engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. An electronically operated throttle allows closed loop engine velocity control. System time delays and nonlinearities render closed loop control design extremely problematic. A model-based controller is designed and shown to be effective in controlling the DC link voltage, resulting in the well-conditioned operation of the hybrid vehicle.

Role of amygdala central nucleus in feature negative discriminations

Science.gov (United States)

Holland, Peter C.

2012-01-01

Consistent with a popular theory of associative learning, the Pearce-Hall (1980) model, the surprising omission of expected events enhances cue associability (the ease with which a cue may enter into new associations), across a wide variety of behavioral training procedures. Furthermore, previous experiments from this laboratory showed that these enhancements are absent in rats with impaired function of the amygdala central nucleus (CeA). A notable exception to these assertions is found in feature negative (FN) discrimination learning, in which a “target” stimulus is reinforced when it is presented alone but nonreinforced when it is presented in compound with another, “feature” stimulus. According to the Pearce-Hall model, reinforcer omission on compound trials should enhance the associability of the feature relative to control training conditions. However, prior experiments have shown no evidence that CeA lesions affect FN discrimination learning. Here we explored this apparent contradiction by evaluating the hypothesis that the surprising omission of an event confers enhanced associability on a cue only if that cue itself generates the disconfirmed prediction. Thus, in a FN discrimination, the surprising omission of the reinforcer on compound trials would enhance the associability of the target stimulus but not that of the feature. Our data confirmed this hypothesis, and showed this enhancement to depend on intact CeA function, as in other procedures. The results are consistent with modern reformulations of both cue and reward processing theories that assign roles for both individual and aggregate error terms in associative learning. PMID:22889308

Biosolar energy generation and harvesting from biomolecule-copolymer hybrid systems

Science.gov (United States)

Chu, Bong-Chieh Benjamin

Alternative energy sources have become an increasingly important topic as energy needs outpace supply. Furthermore, as the world moves into the digital age of portable electronics, highly efficient and lightweight energy sources will need to be developed. Current technology, such as lithium ion batteries, provide enough power to run portable electronics for hours or days, but can still allow for improvement in their power density (W/kg). Utilizing energy-transducing membrane proteins, which are by nature highly efficient, it is possible to engineer biological-based energy sources with energy densities far greater than any solid-state systems. Furthermore, solar powered membrane proteins have the added benefit of a virtually unlimited supply of energy. This work has developed protein-polymer hybrid films and nanoscale vesicles for a variety of applications from fuel-cell technology to biological-based photovoltaics. Bacteriorhodopsin (BR), a light-activated proton pump, and Cytochrome C Oxidase (COX), a protein involved in the electron transport chain in mitochondria, were reconstituted into biomimetic triblock copolymer membranes. Block copolymer membranes mimic the amphiphilic nature of a natural lipid bilayer but exhibit greater mechanical stability due to UV-polymerizable endgroups. In BR/COX functionalized nanovesicles, proton gradients generated by the light-activated proton pumping of BR are used to drive COX in reverse to generate electrons, providing a hybrid biologically-active polymer to convert solar energy to chemical energy, and finally to electrical energy. This work has found protein activity in planar membranes through the photoelectric current generation by BR and the proton pumping activity of BR-functionalized polymer membranes deposited onto proton exchange membranes, as well as the coupled functionality of BR and COX through current generation in cyclic voltammetry and direct current measurements. Current switching between light and dark

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Directory of Open Access Journals (Sweden)

Christoff Fourie

2014-11-01

Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

Communicator Style as a Predictor of Cyberbullying in a Hybrid Learning Environment

Science.gov (United States)

Dursun, Ozcan Ozgur; Akbulut, Yavuz

2012-01-01

This study aimed to describe the characteristics of undergraduate students in a hybrid learning environment with regard to their communicator styles and cyberbullying behaviors. Moreover, relationships between cyberbullying victimization and learners' perceived communicator styles were investigated. Cyberbullying victimization was measured through…

Go/no-go discriminated avoidance learning in prenatally x-irradiated rats

International Nuclear Information System (INIS)

Tamaki, Y.; Inouye, M.

1988-01-01

Male Fischer344 rats were exposed to x-irradiation at a dose of 200 rad on Day 17 of gestation. Irradiated and control rats were tested at 10-13 weeks of age with the paradigm of go/no-go (active-passive) discriminated avoidance conditioning for three consecutive daily sessions. During the first conditioning session, they learned only active avoidance responses to two different warning signals. During the second and third sessions, they learned active and passive avoidance responses: in response to one warning signal, rats were required to make an active response to avoid a shock, but not to run in response to the other signal in order to avoid a shock. Prenatally irradiated rats made more active avoidance responses to both warning signals than controls (first session). In the early training phase of the go/no-go task, irradiated rats performed significantly higher active and lower passive avoidance responses than controls. Irradiated rats established a strong tendency to respond actively to the no-go signal, but eventually learned to respond to it

Valence of facial cues influences sheep learning in a visual discrimination task

OpenAIRE

Bellegarde, Lucille; Erhard, Hans; Weiss, A.; Boissy, Alain; Haskell, M.J.

2017-01-01

Sheep are one of the most studied farm species in terms of their ability to process information from faces, but little is known about their face-based emotion recognition abilities. We investigated (a) whether sheep could use images of sheep faces taken in situation of varying valence as cues in a simultaneous discrimination task and (b) whether the valence of the situation affects their learning performance. To accomplish this, we photographed faces of sheep in three situations inducing emot...

Valence of Facial Cues Influences Sheep Learning in a Visual Discrimination Task

OpenAIRE

Lucille G. A. Bellegarde; Lucille G. A. Bellegarde; Lucille G. A. Bellegarde; Hans W. Erhard; Alexander Weiss; Alain Boissy; Marie J. Haskell

2017-01-01

Sheep are one of the most studied farm species in terms of their ability to process information from faces, but little is known about their face-based emotion recognition abilities. We investigated (a) whether sheep could use images of sheep faces taken in situation of varying valence as cues in a simultaneous discrimination task and (b) whether the valence of the situation affects their learning performance. To accomplish this, we photographed faces of sheep in three situations inducing emot...

A self-sustaining high-strength wastewater treatment system using solar-bio-hybrid power generation.

Science.gov (United States)

Bustamante, Mauricio; Liao, Wei

2017-06-01

This study focuses on system analysis of a self-sustaining high-strength wastewater treatment concept combining solar technologies, anaerobic digestion, and aerobic treatment to reclaim water. A solar bio-hybrid power generation unit was adopted to power the wastewater treatment. Concentrated solar power (CSP) and photovoltaics (PV) were combined with biogas energy from anaerobic digestion. Biogas is also used to store the extra energy generated by the hybrid power unit and ensure stable and continuous wastewater treatment. It was determined from the energy balance analysis that the PV-bio hybrid power unit is the preferred energy unit to realize the self-sustaining high-strength wastewater treatment. With short-term solar energy storage, the PV-bio-hybrid power unit in Phoenix, AZ requires solar collection area (4032m 2 ) and biogas storage (35m 3 ), while the same unit in Lansing, MI needs bigger solar collection area and biogas storage (5821m 2 and 105m 3 , respectively) due to the cold climate. Copyright © 2017 Elsevier Ltd. All rights reserved.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

Science.gov (United States)

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

Design and Analysis of a Linear Hybrid Excitation Flux-Switching Generator for Direct Drive Wave Energy Converters

Directory of Open Access Journals (Sweden)

Lei Huang

2013-01-01

Full Text Available Linear generators have the advantage of a simple structure of the secondary, which is suitable for the application of wave energy conversion. Based on the vernier hybrid machines (VHMs, widely used for direct drive wave energy converters, this paper proposes a novel hybrid excitation flux-switching generator (LHEFSG, which can effectively improve the performance of this kind of generators. DC hybrid excitation windings and multitooth structure were used in the proposed generator to increase the magnetic energy and overcome the disadvantages of easily irreversible demagnetization of VHMs. Firstly, the operation principle and structure of the proposed generator are introduced. Secondly, by using the finite element method, the no-load performance of the proposed generator is analyzed and composed with ones of conventional VHM. In addition, the on-load performance of the proposed generator is obtained by finite element analysis (FEA. A dislocation of pole alignments method is implemented to reduce the cogging force. Lastly, a prototype of the linear flux-switching generator is used to verify the correctness of FEA results. All the results validate that the proposed generator has better performance than its counterparts.

Active Learning Innovations in Knowledge Management Education Generate Higher Quality Learning Outcomes

Directory of Open Access Journals (Sweden)

Arthur Shelley

2014-01-01

Full Text Available Innovations in how a postgraduate course in knowledge management is delivered have generated better learning outcomes and made the course more engaging for learners. Course participant feedback has shown that collaborative active learning is preferred and provides them with richer insights into how knowledge is created and applied to generate innovation and value. The course applies an andragogy approach in which students collaborate in weekly dialogue of their experiences of the content, rather than learn the content itself. The approach combines systems thinking, learning praxis, and active learning to explore the interdependencies between topics and how they impact outcomes in real world situations. This has stimulated students to apply these ideas in their own workplaces.

Tri-generation based hybrid power plant scheduling for renewable resources rich area with energy storage

International Nuclear Information System (INIS)

Pazheri, F.R.

2015-01-01

Highlights: • Involves scheduling of the tri-generation based hybrid power plant. • Utilization of renewable energy through energy storage is discussed. • Benefits of the proposed model are illustrated. • Energy efficient and environmental friendly dispatch is analyzed. • Modeled scheduling problem is applicable to any fuel enriched area. - Abstract: Solving power system scheduling is crucial to ensure smooth operations of the electric power industry. Effective utilization of available conventional and renewable energy sources (RES) by tri-generation and with the aid of energy storage facilities (ESF) can ensure clean and energy efficient power generation. Such power generation can play an important role in countries, like Saudi Arabia, where abundant fossil fuels (FF) and renewable energy sources (RES) are available. Hence, effective modeling of such hybrid power systems scheduling is essential in such countries based on the available fuel resources. The intent of this paper is to present a simple model for tri-generation based hybrid power system scheduling for energy resources rich area in presence of ESF, to ensure optimum fuel utilization and minimum pollutant emissions while meeting the power demand. This research points an effective operation strategy which ensure a clean and energy efficient power scheduling by exploiting available energy resources effectively. Hence, it has an important role in current and future power generation. In order to illustrate the benefits of the presented approach a clean and energy efficient hybrid power supply scheme for King Saud University (KSU), Saudi Arabia, is proposed and analyzed here. Results show that the proposed approach is very suitable for KSU since adequate solar power is available during its peak demand periods

Does Fine Color Discrimination Learning in Free-Flying Honeybees Change Mushroom-Body Calyx Neuroarchitecture?

Directory of Open Access Journals (Sweden)

Frank M J Sommerlandt

Full Text Available Honeybees learn color information of rewarding flowers and recall these memories in future decisions. For fine color discrimination, bees require differential conditioning with a concurrent presentation of target and distractor stimuli to form a long-term memory. Here we investigated whether the long-term storage of color information shapes the neural network of microglomeruli in the mushroom body calyces and if this depends on the type of conditioning. Free-flying honeybees were individually trained to a pair of perceptually similar colors in either absolute conditioning towards one of the colors or in differential conditioning with both colors. Subsequently, bees of either conditioning groups were tested in non-rewarded discrimination tests with the two colors. Only bees trained with differential conditioning preferred the previously learned color, whereas bees of the absolute conditioning group, and a stimuli-naïve group, chose randomly among color stimuli. All bees were then kept individually for three days in the dark to allow for complete long-term memory formation. Whole-mount immunostaining was subsequently used to quantify variation of microglomeruli number and density in the mushroom-body lip and collar. We found no significant differences among groups in neuropil volumes and total microglomeruli numbers, but learning performance was negatively correlated with microglomeruli density in the absolute conditioning group. Based on these findings we aim to promote future research approaches combining behaviorally relevant color learning tests in honeybees under free-flight conditions with neuroimaging analysis; we also discuss possible limitations of this approach.

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

Science.gov (United States)

Friedel, M. J.; Asch, T. H.; Oden, C.

2012-08-01

The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot-Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

Science.gov (United States)

Friedel, M.J.; Asch, T.H.; Oden, C.

2012-01-01

The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the

Experimental study into a hybrid PCCI/CI concept for next-generation heavy-duty diesel engines

NARCIS (Netherlands)

Doosje, E.; Willems, F.P.T.; Baert, R.S.G.; Dijk, M.D. van

2012-01-01

This paper presents the first results of an experimental study into a hybrid combustion concept for next-generation heavy-duty diesel engines. In this hybrid concept, at low load operating conditions, the engine is run in Pre-mixed Charge Compression Ignition (PCCI) mode, whereas at high load

Problem and Project Based Learning in Hybrid Spaces

DEFF Research Database (Denmark)

Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

2016-01-01

There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem...... and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how students incorporate networked and digital technologies into their group work and into the study places...... they create for themselves. We describe how in one of the programmes ‘nomadic’ groups of students used different technologies and spaces for ‘placemaking’. We then show how their experience and approach to collaborative work differs to that of the more static or ‘artisan’ groups of students in the other...

Patterns of Learning in Verbal Discrimination as an Interaction of Social Reinforcement and Sex Combinations

Science.gov (United States)

Ratliff, Richard G.; And Others

1976-01-01

A total of 540 college students were run in two verbal discrimination learning studies (the second, a replication of the first) with one of three verbal reward conditions. In both studies, equal numbers of male and female subjects were run in each reward condition by each male and female experimenter. (MS)

Autoencoding beyond pixels using a learned similarity metric

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo; Sønderby, Søren Kaae; Larochelle, Hugo

2016-01-01

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstr...

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

Science.gov (United States)

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

2014-05-27

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

Directory of Open Access Journals (Sweden)

Zheng-Zhou Li

2014-05-01

Full Text Available It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn’t be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

Wind energy-hydrogen storage hybrid power generation

Energy Technology Data Exchange (ETDEWEB)

Wenjei Yang; Orhan Aydin [University of Michigan, Ann Arbor, MI (United States). Dept. of Mechanical Engineering and Applied Mechanics

2001-07-01

In this theoretical investigation, a hybrid power generation system utilizing wind energy and hydrogen storage is presented. Firstly, the available wind energy is determined, which is followed by evaluating the efficiency of the wind energy conversion system. A revised model of windmill is proposed from which wind power density and electric power output are determined. When the load demand is less than the output of the generation, the excess electric power is relayed to the electrolytic cell where it is used to electrolyse the de-ionized water. Hydrogen thus produced can be stored as hydrogen compressed gas or liquid. Once the hydrogen is stored in an appropriate high-pressure vessel, it can be used in a combustion engine, fuel cell, or burned in a water-cooled burner to produce a very high-quality steam for space heating, or to drive a turbine to generate electric power. It can also be combined with organic materials to produce synthetic fuels. The conclusion is that the system produces no harmful waste and depletes no resources. Note that this system also works well with a solar collector instead of a windmill. (author)

From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination.

Science.gov (United States)

De Meo, Rosanna; Bourquin, Nathalie M-P; Knebel, Jean-François; Murray, Micah M; Clarke, Stephanie

2015-09-01

Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291ms in the left middle frontal gyrus; and iii) 512-545ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra

A hybrid-type quantum random number generator

Science.gov (United States)

Hai-Qiang, Ma; Wu, Zhu; Ke-Jin, Wei; Rui-Xue, Li; Hong-Wei, Liu

2016-05-01

This paper proposes a well-performing hybrid-type truly quantum random number generator based on the time interval between two independent single-photon detection signals, which is practical and intuitive, and generates the initial random number sources from a combination of multiple existing random number sources. A time-to-amplitude converter and multichannel analyzer are used for qualitative analysis to demonstrate that each and every step is random. Furthermore, a carefully designed data acquisition system is used to obtain a high-quality random sequence. Our scheme is simple and proves that the random number bit rate can be dramatically increased to satisfy practical requirements. Project supported by the National Natural Science Foundation of China (Grant Nos. 61178010 and 11374042), the Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications), China, and the Fundamental Research Funds for the Central Universities of China (Grant No. bupt2014TS01).

A possible structural correlate of learning performance on a colour discrimination task in the brain of the bumblebee

Science.gov (United States)

Li, Li; MaBouDi, HaDi; Egertová, Michaela; Elphick, Maurice R.

2017-01-01

Synaptic plasticity is considered to be a basis for learning and memory. However, the relationship between synaptic arrangements and individual differences in learning and memory is poorly understood. Here, we explored how the density of microglomeruli (synaptic complexes) within specific regions of the bumblebee (Bombus terrestris) brain relates to both visual learning and inter-individual differences in learning and memory performance on a visual discrimination task. Using whole-brain immunolabelling, we measured the density of microglomeruli in the collar region (visual association areas) of the mushroom bodies of the bumblebee brain. We found that bumblebees which made fewer errors during training in a visual discrimination task had higher microglomerular density. Similarly, bumblebees that had better retention of the learned colour-reward associations two days after training had higher microglomerular density. Further experiments indicated experience-dependent changes in neural circuitry: learning a colour-reward contingency with 10 colours (but not two colours) does result, and exposure to many different colours may result, in changes to microglomerular density in the collar region of the mushroom bodies. These results reveal the varying roles that visual experience, visual learning and foraging activity have on neural structure. Although our study does not provide a causal link between microglomerular density and performance, the observed positive correlations provide new insights for future studies into how neural structure may relate to inter-individual differences in learning and memory. PMID:28978727

A possible structural correlate of learning performance on a colour discrimination task in the brain of the bumblebee.

Science.gov (United States)

Li, Li; MaBouDi, HaDi; Egertová, Michaela; Elphick, Maurice R; Chittka, Lars; Perry, Clint J

2017-10-11

Synaptic plasticity is considered to be a basis for learning and memory. However, the relationship between synaptic arrangements and individual differences in learning and memory is poorly understood. Here, we explored how the density of microglomeruli (synaptic complexes) within specific regions of the bumblebee ( Bombus terrestris ) brain relates to both visual learning and inter-individual differences in learning and memory performance on a visual discrimination task. Using whole-brain immunolabelling, we measured the density of microglomeruli in the collar region (visual association areas) of the mushroom bodies of the bumblebee brain. We found that bumblebees which made fewer errors during training in a visual discrimination task had higher microglomerular density. Similarly, bumblebees that had better retention of the learned colour-reward associations two days after training had higher microglomerular density. Further experiments indicated experience-dependent changes in neural circuitry: learning a colour-reward contingency with 10 colours (but not two colours) does result, and exposure to many different colours may result, in changes to microglomerular density in the collar region of the mushroom bodies. These results reveal the varying roles that visual experience, visual learning and foraging activity have on neural structure. Although our study does not provide a causal link between microglomerular density and performance, the observed positive correlations provide new insights for future studies into how neural structure may relate to inter-individual differences in learning and memory. © 2017 The Authors.

Expanding photovoltaic penetration with residential distributed generation from hybrid solar photovoltaic and combined heat and power systems

International Nuclear Information System (INIS)

Pearce, J.M.

2009-01-01

The recent development of small scale combined heat and power (CHP) systems has provided the opportunity for in-house power backup of residential-scale photovoltaic (PV) arrays. This paper investigates the potential of deploying a distributed network of PV + CHP hybrid systems in order to increase the PV penetration level in the U.S. The temporal distribution of solar flux, electrical and heating requirements for representative U.S. single family residences were analyzed and the results clearly show that hybridizing CHP with PV can enable additional PV deployment above what is possible with a conventional centralized electric generation system. The technical evolution of such PV + CHP hybrid systems was developed from the present (near market) technology through four generations, which enable high utilization rates of both PV-generated electricity and CHP-generated heat. A method to determine the maximum percent of PV-generated electricity on the grid without energy storage was derived and applied to an example area. The results show that a PV + CHP hybrid system not only has the potential to radically reduce energy waste in the status quo electrical and heating systems, but it also enables the share of solar PV to be expanded by about a factor of five. (author)

Renewable electricity generation in India—A learning rate analysis

International Nuclear Information System (INIS)

Partridge, Ian

2013-01-01

The cost of electricity generation using renewable technologies is widely assumed to be higher than the cost for conventional generation technologies, but likely to fall with growing experience of the technologies concerned. This paper tests the second part of that statement using learning rate analysis, based on large samples of wind and small hydro projects in India, and projects likely changes in these costs through 2020. It is the first study of learning rates for renewable generation technologies in India, and only the second in any developing country—it provides valuable input to the development of Indian energy policy and will be relevant to policy makers in other developing countries. The paper considers some potential problems with learning rate analysis raised by Nordhaus (2009. The Perils of the Learning Model for Modeling Endogenous Technological Change. National Bureau of Economic Research Working Paper Series No. 14638). By taking account of these issues, it is possible both to improve the models used for making cost projections and to examine the potential impact of remaining forecasting problems. - Highlights: • The first learning rate analysis of wind generation costs in India. • Only the second learning rate analysis for wind in any developing country. • Reviews missing variable and related issues in learning rate analysis. • Finds a 17.7% learning rate for wind generation costs in India. • Finds no significant learning effect for small hydro

The Hybrid Advantage: Graduate Student Perspectives of Hybrid Education Courses

Science.gov (United States)

Hall, Sarah; Villareal, Donna

2015-01-01

Hybrid courses combine online and face-to-face learning environments. To organize and teach hybrid courses, instructors must understand the uses of multiple online learning tools and face-toface classroom activities to promote and monitor the progress of students. The purpose of this phenomenological study was to explore the perspectives of…

Design of Stand-Alone Hybrid Power Generation System at Brumbun Beach Tulungagung East Java

Science.gov (United States)

Rahmat, A. N.; Hidayat, M. N.; Ronilaya, F.; Setiawan, A.

2018-04-01

Indonesian government insists to optimize the use of renewable energy resources in electricity generation. One of the efforts is launching Independent Energy Village plan. This program aims to fulfill the need of electricity for isolated or remote villages in Indonesia. In order to support the penetration of renewable energy resources in electricity generation, a hybrid power generation system is developed. The simulation in this research is based on the availability of renewable energy resources in Brumbun beach, Tulungagung, East Java. Initially, the electricity was supplied through stand-alone electricity generations which are installed at each house. Hence, the use of electricity between 5 p.m. – 9 p.m. requires high operational costs. Based on the problem above, this research is conducted to design a stand-alone hybrid electricity generation system, which may consist of diesel, wind, and photovoltaic. The design is done by using HOMER software to optimize the use of electricity from renewable resources and to reduce the operation of diesel generation. The combination of renewable energy resources in electricity generation resulted in NPC of 44.680, COE of 0,268, and CO2 emissions of 0,038 % much lower than the use of diesel generator only.

Investigation of thermodynamic performances for two solar-biomass hybrid combined cycle power generation systems

International Nuclear Information System (INIS)

Liu, Qibin; Bai, Zhang; Wang, Xiaohe; Lei, Jing; Jin, Hongguang

2016-01-01

Highlights: • Two solar-biomass hybrid combined cycle power generation systems are proposed. • The characters of the two proposed systems are compared. • The on-design and off-design properties of the system are numerically investigated. • The favorable performances of thermochemical hybrid routine are validated. - Abstract: Two solar-biomass hybrid combined cycle power generation systems are proposed in this work. The first system employs the thermochemical hybrid routine, in which the biomass gasification is driven by the concentrated solar energy, and the gasified syngas as a solar fuel is utilized in a combined cycle for generating power. The second system adopts the thermal integration concept, and the solar energy is directly used to heat the compressed air in the topping Brayton cycle. The thermodynamic performances of the developed systems are investigated under the on-design and off-design conditions. The advantages of the hybrid utilization technical mode are demonstrated. The solar energy can be converted and stored into the chemical fuel by the solar-biomass gasification, with the net solar-to-fuel efficiency of 61.23% and the net solar share of 19.01% under the specific gasification temperature of 1150 K. Meanwhile, the proposed system with the solar thermochemical routine shows more favorable behaviors, the annual system overall energy efficiency and the solar-to-electric efficiency reach to 29.36% and 18.49%, while the with thermal integration concept of 28.03% and 15.13%, respectively. The comparison work introduces a promising approach for the efficient utilization of the abundant solar and biomass resources in the western China, and realizes the mitigation of CO_2 emission.

Pigeons can discriminate "good" and "bad" paintings by children.

Science.gov (United States)

Watanabe, Shigeru

2010-01-01

Humans have the unique ability to create art, but non-human animals may be able to discriminate "good" art from "bad" art. In this study, I investigated whether pigeons could be trained to discriminate between paintings that had been judged by humans as either "bad" or "good". To do this, adult human observers first classified several children's paintings as either "good" (beautiful) or "bad" (ugly). Using operant conditioning procedures, pigeons were then reinforced for pecking at "good" paintings. After the pigeons learned the discrimination task, they were presented with novel pictures of both "good" and "bad" children's paintings to test whether they had successfully learned to discriminate between these two stimulus categories. The results showed that pigeons could discriminate novel "good" and "bad" paintings. Then, to determine which cues the subjects used for the discrimination, I conducted tests of the stimuli when the paintings were of reduced size or grayscale. In addition, I tested their ability to discriminate when the painting stimuli were mosaic and partial occluded. The pigeons maintained discrimination performance when the paintings were reduced in size. However, discrimination performance decreased when stimuli were presented as grayscale images or when a mosaic effect was applied to the original stimuli in order to disrupt spatial frequency. Thus, the pigeons used both color and pattern cues for their discrimination. The partial occlusion did not disrupt the discriminative behavior suggesting that the pigeons did not attend to particular parts, namely upper, lower, left or right half, of the paintings. These results suggest that the pigeons are capable of learning the concept of a stimulus class that humans name "good" pictures. The second experiment showed that pigeons learned to discriminate watercolor paintings from pastel paintings. The subjects showed generalization to novel paintings. Then, as the first experiment, size reduction test

White light generation tuned by dual hybridization of nanocrystals and conjugated polymers

International Nuclear Information System (INIS)

Demir, Hilmi Volkan; Nizamoglu, Sedat; Ozel, Tuncay; Mutlugun, Evren; Huyal, Ilkem Ozge; Sari, Emre; Holder, Elisabeth; Tian Nan

2007-01-01

Dual hybridization of highly fluorescent conjugated polymers and highly luminescent nanocrystals (NCs) is developed and demonstrated in multiple combinations for controlled white light generation with high color rendering index (CRI) (> 80) for the first time. The generated white light is tuned using layer-by-layer assembly of CdSe/ZnS core-shell NCs closely packed on polyfluorene, hybridized on near-UV emitting nitride-based light emitting diodes (LEDs). The design, synthesis, growth, fabrication and characterization of these hybrid inorganic-organic white LEDs are presented. The following experimental realizations are reported: (i) layer-by-layer hybridization of yellow NCs (λ PL =580 nm) and blue polyfluorene (λ PL =439 nm) with tristimulus coordinates of (x, y)=(0.31, 0.27), correlated color temperature of T c =6962 K and CRI of R a =53.4; (ii) layer-by-layer assembly of yellow and green NCs (λ PL =580 and 540 nm) and blue polyfluorene (λ PL =439 nm) with (x, y)=(0.23, 0.30), T c =14395 K and R a =65.7; and (iii) layer-by-layer deposition of yellow, green and red NCs (λ PL =580, 540 and 620 nm) and blue polyfluorene (λ PL =439 nm) with (x, y)=(0.38, 0.39), T c =4052 K and R a = 83.0. The CRI is demonstrated to be well controlled and significantly improved by increasing multi-chromaticity of the NC and polymer emitters

Sustainable electricity generation by solar pv/diesel hybrid system without storage for off grids areas

Science.gov (United States)

Azoumah, Y.; Yamegueu, D.; Py, X.

2012-02-01

Access to energy is known as a key issue for poverty reduction. The electrification rate of sub Saharan countries is one of the lowest among the developing countries. However this part of the world has natural energy resources that could help raising its access to energy, then its economic development. An original "flexy energy" concept of hybrid solar pv/diesel/biofuel power plant, without battery storage, is developed in order to not only make access to energy possible for rural and peri-urban populations in Africa (by reducing the electricity generation cost) but also to make the electricity production sustainable in these areas. Some experimental results conducted on this concept prototype show that the sizing of a pv/diesel hybrid system by taking into account the solar radiation and the load/demand profile of a typical area may lead the diesel generator to operate near its optimal point (70-90 % of its nominal power). Results also show that for a reliability of a PV/diesel hybrid system, the rated power of the diesel generator should be equal to the peak load. By the way, it has been verified through this study that the functioning of a pv/Diesel hybrid system is efficient for higher load and higher solar radiation.

Sustainable electricity generation by solar pv/diesel hybrid system without storage for off grids areas

International Nuclear Information System (INIS)

Azoumah, Y; Yamegueu, D; Py, X

2012-01-01

Access to energy is known as a key issue for poverty reduction. The electrification rate of sub Saharan countries is one of the lowest among the developing countries. However this part of the world has natural energy resources that could help raising its access to energy, then its economic development. An original 'flexy energy' concept of hybrid solar pv/diesel/biofuel power plant, without battery storage, is developed in order to not only make access to energy possible for rural and peri-urban populations in Africa (by reducing the electricity generation cost) but also to make the electricity production sustainable in these areas. Some experimental results conducted on this concept prototype show that the sizing of a pv/diesel hybrid system by taking into account the solar radiation and the load/demand profile of a typical area may lead the diesel generator to operate near its optimal point (70-90 % of its nominal power). Results also show that for a reliability of a PV/diesel hybrid system, the rated power of the diesel generator should be equal to the peak load. By the way, it has been verified through this study that the functioning of a pv/Diesel hybrid system is efficient for higher load and higher solar radiation.

Optimizing the specificity of nucleic acid hybridization.

Science.gov (United States)

Zhang, David Yu; Chen, Sherry Xi; Yin, Peng

2012-01-22

The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse temperature, salt and concentration conditions. We rationally designed 'toehold exchange' probes that approximate these properties, and comprehensively tested them against five different DNA targets and 55 spurious analogues with energetically representative single-base changes (replacements, deletions and insertions). These probes produced discrimination factors between 3 and 100+ (median, 26). Without retuning, our probes function robustly from 10 °C to 37 °C, from 1 mM Mg(2+) to 47 mM Mg(2+), and with nucleic acid concentrations from 1 nM to 5 µM. Experiments with RNA also showed effective single-base change discrimination.

Mobile e-Learning for Next Generation Communication Environment

Science.gov (United States)

Wu, Tin-Yu; Chao, Han-Chieh

2008-01-01

This article develops an environment for mobile e-learning that includes an interactive course, virtual online labs, an interactive online test, and lab-exercise training platform on the fourth generation mobile communication system. The Next Generation Learning Environment (NeGL) promotes the term "knowledge economy." Inter-networking…

Faster native vowel discrimination learning in musicians is mediated by an optimization of mnemonic functions.

Science.gov (United States)

Elmer, Stefan; Greber, Marielle; Pushparaj, Arethy; Kühnis, Jürg; Jäncke, Lutz

2017-09-01

The ability to discriminate phonemes varying in spectral and temporal attributes constitutes one of the most basic intrinsic elements underlying language learning mechanisms. Since previous work has consistently shown that professional musicians are characterized by perceptual and cognitive advantages in a variety of language-related tasks, and since vowels can be considered musical sounds within the domain of speech, here we investigated the behavioral and electrophysiological correlates of native vowel discrimination learning in a sample of professional musicians and non-musicians. We evaluated the contribution of both the neurophysiological underpinnings of perceptual (i.e., N1/P2 complex) and mnemonic functions (i.e., N400 and P600 responses) while the participants were instructed to judge whether pairs of native consonant-vowel (CV) syllables manipulated in the first formant transition of the vowel (i.e., from /tu/ to /to/) were identical or not. Results clearly demonstrated faster learning in musicians, compared to non-musicians, as reflected by shorter reaction times and higher accuracy. Most notably, in terms of morphology, time course, and voltage strength, this steeper learning curve was accompanied by distinctive N400 and P600 manifestations between the two groups. In contrast, we did not reveal any group differences during the early stages of auditory processing (i.e., N1/P2 complex), suggesting that faster learning was mediated by an optimization of mnemonic but not perceptual functions. Based on a clear taxonomy of the mnemonic functions involved in the task, results are interpreted as pointing to a relationship between faster learning mechanisms in musicians and an optimization of echoic (i.e., N400 component) and working memory (i.e., P600 component) functions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Numerical investigation of heat pipe-based photovoltaic–thermoelectric generator (HP-PV/TEG) hybrid system

International Nuclear Information System (INIS)

Makki, Adham; Omer, Siddig; Su, Yuehong; Sabir, Hisham

2016-01-01

Highlights: • Integration of TE generators with a heat pipe-based PV module as a hybrid system is proposed. • Numerical transient modeling based on the energy balance equations of the system was performed. • Integration of TE generators with PV module aid operating the solar cells at a steady level in harsh conditions. - Abstract: Photovoltaic (PV) cells are able to absorb about 80% of the solar spectral irradiance, however, certain percentage accounts for electricity conversion depending on the cell technology employed. The remainder energy however, can elevate the silicon junction temperature in the PV encapsulation perilously, resulting in deteriorated performance. Temperature rise at the PV cell level is addressed as one of the most critical issues that can seriously degrade and shortens the life-time of the PV cells, hence thermal management of the PV module during operation is considered essential. Hybrid PV designs which are able to simultaneously generate electrical energy and utilize the waste heat have been proven to be the most promising solution. In this study, theoretical investigation of a hybrid system comprising of thermoelectric generator integration with a heat pipe-based Photovoltaic/Thermal (PV/T) absorber is proposed and evaluated. The system presented incorporates a PV panel for direct electricity generation, a heat pipe for excessive heat absorption from the PV cells and a thermoelectric generator (TEG) performing direct heat-to-electricity conversion. A mathematical model based on the energy balance within the system is developed to evaluate the performance of the hybrid integration and the improvements associated with the thermal management of PV cells. Results are presented in terms of the overall system efficiency compared to a conventional PV panel under identical operating conditions. The integration of TEG modules with PV cells in such way aid improving the performance of the PV cells in addition to utilizing the waste

Lower-hybrid wave coupling and impurity generation in Tore Supra

International Nuclear Information System (INIS)

Goniche, M.; Litaudon, X.; Guilhem, D.; Hutter, T.; Beaumont, B.; Froissart, P.; Rey, G.; Saoutic, B.

1995-01-01

This document deals with the high power coupling of Lower Hybrid (LH) waves in Tore Supra. The effect of the plasma shape is described, together with LH coupling in ion-cyclotron resonance experiments. It appears that plasma modifications can alter the LH coupling. Eventually, the effect of LH power on thermal load and impurity generation is presented. (TEC). 3 refs., 3 figs

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

Science.gov (United States)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

Pulse-width discriminators

International Nuclear Information System (INIS)

Budyashov, Yu.G.; Grebenyuk, V.M.; Zinov, V.G.

1978-01-01

A pulse duration discriminator is described which is intended for processing signals from multilayer scintillators. The basic elements of the scintillator are: an input gate, a current generator, an integrating capacitor, a Schmidt trigger and an anticoincidence circuit. The basic circuit of the discriminator and its time diagrams explaining its operating are given. The discriminator is based on microcircuits. Pulse duration discrimination threshold changes continuously from 20 to 100 ns, while its amplitude threshold changes within 20 to 100 mV. The temperature instability of discrimination thresholds (both in pulse width and in amplitude) is better than 0.1 per cent/deg C

Students' Perceptions of Learning Mode in Mathematics

Science.gov (United States)

Krishnan, Saras

2016-01-01

Blended courses or hybrid courses have gained popularity over the years because of their flexibility and convenience. Technology use in the online component of the blended/hybrid courses is another influence particularly to the younger generation of learners who enjoy learning interactively in a virtual environment. However, depending on the…

An experimental study on energy generation with a photovoltaic (PV)-solar thermal hybrid system

International Nuclear Information System (INIS)

Erdil, Erzat; Ilkan, Mustafa; Egelioglu, Fuat

2008-01-01

A hybrid system, composed of a photovoltaic (PV) module and a solar thermal collector is constructed and tested for energy collection at a geographic location of Cyprus. Normally, it is required to install a PV system occupying an area of about 10 m 2 in order to produce electrical energy; 7 kWh/day, required by a typical household. In this experimental study, we used only two PV modules of area approximately 0.6 m 2 (i.e., 1.3x0.47 m 2 ) each. PV modules absorb a considerable amount of solar radiation that generate undesirable heat. This thermal energy, however, may be utilized in water pre-heating applications. The proposed hybrid system produces about 2.8 kWh thermal energy daily. Various attachments that are placed over the hybrid modules lead to a total of 11.5% loss in electrical energy generation. This loss, however, represents only 1% of the 7 kWh energy that is consumed by a typical household in northern Cyprus. The pay-back period for the modification is less than 2 years. The low investment cost and the relatively short pay-back period make this hybrid system economically attractive

Simulation and Parametric Analysis of a Hybrid SOFC-Gas Turbine Power Generation System

International Nuclear Information System (INIS)

Hassan, A.M.; Fahmy

2004-01-01

Combined SOFC-Gas Turbine Power Generation Systems are aimed to increase the power and efficiency obtained from the technology of using high temperature fuel cells by integrating them with gas turbines. Hybrid systems are considered in the last few years as one of the most promising technologies to obtain electric energy from the natural gas at very high efficiency with a serious potential for commercial use. The use of high temperature allows internal reforming for natural gas and thus disparity of fuel composition is allowed. Also air preheating is performed thanks to the high operating cell temperature as a task of energy integration. In this paper a modeling approach is presented for the fuel cell-gas turbine hybrid power generation systems, to obtain the sofc output voltage, power, and the overall hybrid system efficiency. The system has been simulated using HYSYS, the process simulation software to help improving the process understanding and provide a quick system solution. Parametric analysis is also presented in this paper to discuss the effect of some important SOFC operating parameters on the system performance and efficiency

New approach to improve the energy density of hybrid electret-dielectric elastomer generators

Science.gov (United States)

Lagomarsini, Clara; Jean-Mistral, Claire; Monfray, Stephane; Sylvestre, Alain

2017-04-01

Harvesting human kinetic energy to produce electricity is an attractive alternative to batteries for applications in wearable electronic devices and smart textile. Dielectric elastomers generators (DEGs) represent one of the most promising technologies for these applications. Nevertheless, one of the main disadvantages of these structures is the need of an external polarization source to perform the energetic cycle. In the present work, a hybrid electret-dielectric elastomer generator in DEG mode is presented. In this configuration, the electret material is used as polarization source of a classical DEG, i.e. an electrostatic generator based on electrical capacitance variation. The electrical energy output in this mode (1.06mJ.g-1) could be higher than the one obtained using a classical electret mode (0.55mJ.g-1), i.e. charges recombination. In this paper, the operation principle of the hybrid generator will be fully described and the design rules for the realization of the prototype will be presented. The experimental data obtained from the prototype will be compared to the results of FEM simulations.

Hybrid layer difference between sixth and seventh generation bonding agent

Directory of Open Access Journals (Sweden)

Grace Syavira Suryabrata

2006-03-01

Full Text Available Since etching is completed at the same stage as priming and bonding, when applying the sixth and seventh generation bonding, the exposed smear layers are constantly surrounded by primer and bonding and cannot collapse. The smear layer and the depth of penetration of resin bonding in dentinal tubules are completely integrated into hybrid layer. The purpose of this laboratory research was to study the penetration depth of two self etching adhesive. Fourteen samples of human extracted teeth were divided into two groups. Each groups consisted of seven samples, each of them was treated with sixth generation bonding agent and the other was treated with seventh generation bonding agent. The results disclosed that the penetration into dentinal tubules of seventh generation bonding agent was deeper than sixth generation bonding agent. Conclusion: bond strength will improve due to the increasing of penetration depth of resin bonding in dentinal tubules.

Hierarchical Discriminant Analysis

Directory of Open Access Journals (Sweden)

Di Lu

2018-01-01

Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

Generation of runaway electrons during deterioration of lower hybrid power coupling in lower hybrid current drive plasmas in the HT-7 tokamak

International Nuclear Information System (INIS)

Chen, Z Y; Ju, H J; Zhu, J X; Li, M; Cai, W D; Liang, H F; Wan, B N; Shi, Y J; Xu, H D

2009-01-01

Efficient coupling of lower hybrid (LH) power from the wave launcher to the plasma is a very important issue in lower hybrid current drive (LHCD) experiments. The large unbalanced reflections in the grill trigger the LH protection system, which will trip the power, resulting in the reduction of the coupled LH power. The generation of runaway electrons has been investigated in LHCD plasmas with deterioration of LH coupling in the HT-7 tokamak. The deterioration of LH coupling results in an increase of the loop voltage and a more energetic fast electron population. These two effects favor the generation of a runaway population. It is found that most of the fast electrons generated by LH waves through parallel electron Landau damping were converted into a runaway population through the acceleration from the toroidal electric field when significant deterioration of LH coupling occurs.

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Pressurisation of IP-SOFC technology for second generation hybrid application

Energy Technology Data Exchange (ETDEWEB)

Jones, L.

2005-07-01

The Integrated Planar Solid Oxide Fuel Cell (IP-SOFC) technology developed by Rolls-Royce plc is a hybrid fuel cell technology considered highly suitable for the distributed power generation market. This report presents the results of a project to examine the technical viability of the IP-SOFC technology and some of the associated hybrid system component technologies under pressurised conditions and to investigate the validity of the predicted pressurisation phenomena. The work included: identification of critical material specifications, construction processes, control parameters, etc; the design and commissioning of two pressurised IP-SOFC test rigs at Rolls Royce in Derby; testing two multi-bundle strips at high temperature and atmospheric pressure; testing an active IP-SOFC bundle at high temperature and pressure; testing an experimental steam reforming unit at high temperature and pressure; testing a novel low pressure drop, off-gas combustor concept under atmospheric and pressurised conditions; design studies to identify key parameters affecting the successful integration and packaging of the fuel cell stack with certain associated hybrid components; and designing a hybrid system experimental verification rig. Significant progress was made in addressing the development challenges associated with the IP-SOFC of leakage, performance, durability, yield and geometry, the reaction rate of steam reforming and emissions from the off-gas combustor. Recommendations for future work are made.

Assessing the effectiveness of a hybrid-flipped model of learning on fluid mechanics instruction: overall course performance, homework, and far- and near-transfer of learning

Science.gov (United States)

Harrison, David J.; Saito, Laurel; Markee, Nancy; Herzog, Serge

2017-11-01

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity score method entailing a two-stage regression analysis that considered eight covariates to address the potential bias of treatment selection. First, we estimated the probability score based on the eight covariates, and second, we used the inverse of the probability score as a regression weight on the performance of learners who did not select into the hybrid course. Results suggest that enrolment in the hybrid-flipped section had a marginally significant negative impact on the total course score and a significant negative impact on homework performance, possibly because of poor video usage by the hybrid-flipped learners. Suggested considerations are also discussed.

Reconfiguration of photovoltaic panels for reducing the hydrogen consumption in fuel cells of hybrid systems

Directory of Open Access Journals (Sweden)

Daniel González-Montoya

2017-05-01

Full Text Available Hybrid generation combines advantages from fuel cell systems with non-predictable generation approaches, such as photovoltaic and wind generators. In such hybrid systems, it is desirable to minimize as much as possible the fuel consumption, for the sake of reducing costs and increasing the system autonomy. This paper proposes an optimization algorithm, referred to as population-based incremental learning, in order to maximize the produced power of a photovoltaic generator. This maximization reduces the fuel consumption in the hybrid aggregation. Moreover, the algorithm's speed enables the real-time computation of the best configuration for the photovoltaic system, which also optimizes the fuel consumption in the complementary fuel cell system. Finally, a system experimental validation is presented considering 6 photovoltaic modules and a NEXA 1.2KW fuel cell. Such a validation demonstrates the effectiveness of the proposed algorithm to reduce the hydrogen consumption in these hybrid systems.

Understanding of Foreign Language Learning of Generation Y

Science.gov (United States)

Bozavli, Ebubekir

2016-01-01

Different generations are constituted depending on social changes and they are designed sociologically as traditional, baby boomer, X, Y and Z. Many studies have been reported on understanding of foreign language learning generation Y. This study aims to realise the gap in and contribute to the research on language learning understanding of…

Pneumatic hybridization of a diesel engine using compressed air storage for wind-diesel energy generation

International Nuclear Information System (INIS)

Basbous, Tammam; Younes, Rafic; Ilinca, Adrian; Perron, Jean

2012-01-01

In this paper, we are studying an innovative solution to reduce fuel consumption and production cost for electricity production by Diesel generators. The solution is particularly suitable for remote areas where the cost of energy is very high not only because of inherent cost of technology but also due to transportation costs. It has significant environmental benefits as the use of fossil fuels for electricity generation is a significant source of GHG (Greenhouse Gas) emissions. The use of hybrid systems that combine renewable sources, especially wind, and Diesel generators, reduces fuel consumption and operation cost and has environmental benefits. Adding a storage element to the hybrid system increases the penetration level of the renewable sources, that is the percentage of renewable energy in the overall production, and further improves fuel savings. In a previous work, we demonstrated that CAES (Compressed Air Energy Storage) has numerous advantages for hybrid wind-diesel systems due to its low cost, high power density and reliability. The pneumatic hybridization of the Diesel engine consists to introduce the CAES through the admission valve. We have proven that we can improve the combustion efficiency and therefore the fuel consumption by optimizing Air/Fuel ratio thanks to the CAES assistance. As a continuation of these previous analyses, we studied the effect of the intake pressure and temperature and the exhaust pressure on the thermodynamic cycle of the diesel engine and determined the values of these parameters that will optimize fuel consumption. -- Highlights: ► Fuel economy analysis of a simple pneumatic hybridization of the Diesel engine using stored compressed air. ► Thermodynamic analysis of the pneumatic hybridization of diesel engines for hybrid wind-diesel energy systems. ► Analysis of intake pressure and temperature of compressed air and exhaust pressure on pressure/temperature during Diesel thermodynamic cycle. ► Direct admission of

Generative Inferences Based on Learned Relations

Science.gov (United States)

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

Digital-Analog Hybrid Scheme and Its Application to Chaotic Random Number Generators

Science.gov (United States)

Yuan, Zeshi; Li, Hongtao; Miao, Yunchi; Hu, Wen; Zhu, Xiaohua

2017-12-01

Practical random number generation (RNG) circuits are typically achieved with analog devices or digital approaches. Digital-based techniques, which use field programmable gate array (FPGA) and graphics processing units (GPU) etc. usually have better performances than analog methods as they are programmable, efficient and robust. However, digital realizations suffer from the effect of finite precision. Accordingly, the generated random numbers (RNs) are actually periodic instead of being real random. To tackle this limitation, in this paper we propose a novel digital-analog hybrid scheme that employs the digital unit as the main body, and minimum analog devices to generate physical RNs. Moreover, the possibility of realizing the proposed scheme with only one memory element is discussed. Without loss of generality, we use the capacitor and the memristor along with FPGA to construct the proposed hybrid system, and a chaotic true random number generator (TRNG) circuit is realized, producing physical RNs at a throughput of Gbit/s scale. These RNs successfully pass all the tests in the NIST SP800-22 package, confirming the significance of the scheme in practical applications. In addition, the use of this new scheme is not restricted to RNGs, and it also provides a strategy to solve the effect of finite precision in other digital systems.

White light generation tuned by dual hybridization of nanocrystals and conjugated polymers

Energy Technology Data Exchange (ETDEWEB)

Demir, Hilmi Volkan [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Nizamoglu, Sedat [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Ozel, Tuncay [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Mutlugun, Evren [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Huyal, Ilkem Ozge [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Sari, Emre [Devices and Sensors Group and Nanotechnology Research Center, Bilkent University, Ankara 06800 (Turkey); Holder, Elisabeth [Functional Polymers Group and Institute of Polymer Technology, University of Wuppertal, Gaussstrasse 20, D-42097 Wuppertal (Germany); Tian Nan [Functional Polymers Group and Institute of Polymer Technology, University of Wuppertal, Gaussstrasse 20, D-42097 Wuppertal (Germany)

2007-10-15

Dual hybridization of highly fluorescent conjugated polymers and highly luminescent nanocrystals (NCs) is developed and demonstrated in multiple combinations for controlled white light generation with high color rendering index (CRI) (> 80) for the first time. The generated white light is tuned using layer-by-layer assembly of CdSe/ZnS core-shell NCs closely packed on polyfluorene, hybridized on near-UV emitting nitride-based light emitting diodes (LEDs). The design, synthesis, growth, fabrication and characterization of these hybrid inorganic-organic white LEDs are presented. The following experimental realizations are reported: (i) layer-by-layer hybridization of yellow NCs ({lambda}{sub PL}=580 nm) and blue polyfluorene ({lambda}{sub PL}=439 nm) with tristimulus coordinates of (x, y)=(0.31, 0.27), correlated color temperature of T{sub c}=6962 K and CRI of R{sub a}=53.4; (ii) layer-by-layer assembly of yellow and green NCs ({lambda}{sub PL}=580 and 540 nm) and blue polyfluorene ({lambda}{sub PL}=439 nm) with (x, y)=(0.23, 0.30), T{sub c}=14395 K and R{sub a}=65.7; and (iii) layer-by-layer deposition of yellow, green and red NCs ({lambda}{sub PL}=580, 540 and 620 nm) and blue polyfluorene ({lambda}{sub PL}=439 nm) with (x, y)=(0.38, 0.39), T{sub c}=4052 K and R{sub a}= 83.0. The CRI is demonstrated to be well controlled and significantly improved by increasing multi-chromaticity of the NC and polymer emitters.

Learning Dictionaries of Discriminative Image Patches

DEFF Research Database (Denmark)

Dahl, Anders Lindbjerg; Larsen, Rasmus

2011-01-01

using dictionaries of image patches with associated label data. The approach is based on ideas from sparse generative image models and texton based texture modeling. The intensity and label dictionaries are learned from training images with associated label information of (a subset) of the pixels based...... on a modified vector quantization approach. For new images the intensity dictionary is used to encode the image data and the label dictionary is used to build a segmentation of the image. We demonstrate the algorithm on composite and real texture images and show how successful training is possible even...

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Directory of Open Access Journals (Sweden)

Radhika M. Pai

2016-03-01

Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Directory of Open Access Journals (Sweden)

Radhika M. Pai

2016-04-01

Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

Validation of microsatellite multiplexes for parentage analysis and species discrimination in two hybridizing species of coral reef fish (Plectropomus spp., Serranidae)

KAUST Repository

Harrison, H.B.

2014-04-24

Microsatellites are often considered ideal markers to investigate ecological processes in animal populations. They are regularly used as genetic barcodes to identify species, individuals, and infer familial relationships. However, such applications are highly sensitive the number and diversity of microsatellite markers, which are also prone to error. Here, we propose a novel framework to assess the suitability of microsatellite datasets for parentage analysis and species discrimination in two closely related species of coral reef fish, Plectropomus leopardus and P. maculatus (Serranidae). Coral trout are important fisheries species throughout the Indo-Pacific region and have been shown to hybridize in parts of the Great Barrier Reef, Australia. We first describe the development of 25 microsatellite loci and their integration to three multiplex PCRs that co-amplify in both species. Using simulations, we demonstrate that the complete suite of markers provides appropriate power to discriminate between species, detect hybrid individuals, and resolve parent-offspring relationships in natural populations, with over 99.6% accuracy in parent-offspring assignments. The markers were also tested on seven additional species within the Plectropomus genus with polymorphism in 28-96% of loci. The multiplex PCRs developed here provide a reliable and cost-effective strategy to investigate evolutionary and ecological dynamics and will be broadly applicable in studies of wild populations and aquaculture brood stocks for these closely related fish species. 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Validation of microsatellite multiplexes for parentage analysis and species discrimination in two hybridizing species of coral reef fish (Plectropomus spp., Serranidae)

KAUST Repository

Harrison, H.B.; Feldheim, K.A.; Jones, G.P.; Ma, K.; Mansour, H.; Perumal, S.; Williamson, D.H.; Berumen, Michael L.

2014-01-01

Microsatellites are often considered ideal markers to investigate ecological processes in animal populations. They are regularly used as genetic barcodes to identify species, individuals, and infer familial relationships. However, such applications are highly sensitive the number and diversity of microsatellite markers, which are also prone to error. Here, we propose a novel framework to assess the suitability of microsatellite datasets for parentage analysis and species discrimination in two closely related species of coral reef fish, Plectropomus leopardus and P. maculatus (Serranidae). Coral trout are important fisheries species throughout the Indo-Pacific region and have been shown to hybridize in parts of the Great Barrier Reef, Australia. We first describe the development of 25 microsatellite loci and their integration to three multiplex PCRs that co-amplify in both species. Using simulations, we demonstrate that the complete suite of markers provides appropriate power to discriminate between species, detect hybrid individuals, and resolve parent-offspring relationships in natural populations, with over 99.6% accuracy in parent-offspring assignments. The markers were also tested on seven additional species within the Plectropomus genus with polymorphism in 28-96% of loci. The multiplex PCRs developed here provide a reliable and cost-effective strategy to investigate evolutionary and ecological dynamics and will be broadly applicable in studies of wild populations and aquaculture brood stocks for these closely related fish species. 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Science.gov (United States)

Aoki, Kenichi; Wakano, Joe Yuichiro; Lehmann, Laurent

2012-06-01

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium. Copyright © 2012 Elsevier Inc. All rights reserved.

Pattern recognition in bees : orientation discrimination

NARCIS (Netherlands)

Hateren, J.H. van; Srinivasan, M.V.; Wait, P.B.

1990-01-01

Honey bees (Apis mellifera, worker) were trained to discriminate between two random gratings oriented perpendicularly to each other. This task was quickly learned with vertical, horizontal, and oblique gratings. After being trained on perpendicularly-oriented random gratings, bees could discriminate

Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

Science.gov (United States)

Abdolmaleki, Azizeh; Ghasemi, Jahan B

2017-01-01

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Fully Packaged Blue Energy Harvester by Hybridizing a Rolling Triboelectric Nanogenerator and an Electromagnetic Generator.

Science.gov (United States)

Wang, Xin; Wen, Zhen; Guo, Hengyu; Wu, Changsheng; He, Xu; Lin, Long; Cao, Xia; Wang, Zhong Lin

2016-12-27

Ocean energy, in theory, is an enormous clean and renewable energy resource that can generate electric power much more than that required to power the entire globe without adding any pollution to the atmosphere. However, owing to a lack of effective technology, such blue energy is almost unexplored to meet the energy requirement of human society. In this work, a fully packaged hybrid nanogenerator consisting of a rolling triboelectric nanogenerator (R-TENG) and an electromagnetic generator (EMG) is developed to harvest water motion energy. The outstanding output performance of the R-TENG (45 cm 3 in volume and 28.3 g in weight) in the low-frequency range (hybrid nanogenerator to deliver valuable outputs in a broad range of operation frequencies. Therefore, the hybrid nanogenerator can maximize the energy conversion efficiency and broaden the operating frequency simultaneously. In terms of charging capacitors, this hybrid nanogenerator provides not only high voltage and consistent charging from the TENG component but also fast charging speed from the EMG component. The practical application of the hybrid nanogenerator is also demonstrated to power light-emitting diodes by harvesting energy from stimulated tidal flow. The high robustness of the R-TENG is also validated based on the stable electrical output after continuous rolling motion. Therefore, the hybrid R-TENG and EMG device renders an effective and sustainable approach toward large-scale blue energy harvesting in a broad frequency range.

Current generation by unidirectional lower hybrid waves in the ACT-1 toroidal device

International Nuclear Information System (INIS)

Wong, K.L.; Horton, R.; Ono, M.

1980-05-01

An unambiguious experimental observation of current generation by unidirectional lower hybrid waves in a toroidal plasma is reported. Up to 10 amperes of current was driven by 500 watts of rf power at 160 MHz

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

OpenAIRE

Yue Hu; Weimin Li; Kun Xu; Taimoor Zahid; Feiyan Qin; Chenming Li

2018-01-01

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learn...

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

OpenAIRE

Peng, Baolin; Li, Xiujun; Gao, Jianfeng; Liu, Jingjing; Chen, Yun-Nung; Wong, Kam-Fai

2017-01-01

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the...

Techno-economic analysis of an optimized photovoltaic and diesel generator hybrid power system for remote houses in a tropical climate

International Nuclear Information System (INIS)

Ismail, M.S.; Moghavvemi, M.; Mahlia, T.M.I.

2013-01-01

Highlights: ► We analyzed solar data in the location under consideration. ► We developed a program to simulate the operation of the PV-diesel generator hybrid system. ► We analyzed different scenarios to select and design the optimal system. ► It is cost effective to power houses in remote areas with such hybrid systems. ► The hybrid system had lower CO 2 emissions compared to a diesel generator only operation. - Abstract: A techno-economic analysis and the design of a complete hybrid system, consisting of photovoltaic (PV) panels, a battery system and a diesel generator as a backup power source for a typical Malaysian village household is presented in this paper. The specifications of the different components constructing the hybrid system were also determined. A scenario depending on a standalone PV and other scenario depending on a diesel generator only were also analyzed. A simulation program was developed to simulate the operation of these different scenarios. The scenario that achieves the minimum cost while meeting the load requirement was selected. The optimal tilt angle of the PV panels in order to increase the generated energy was obtained using genetic algorithm. In addition, sensitivity analysis was undertaken to evaluate the effect of change of some parameters on the cost of energy. The results indicated that the optimal scenario is the one that consists of a combination of the PV panels, battery bank and a diesel generator. Powering a rural house using this hybrid system is advantageous as it decreases operating cost, increases efficiencies, and reduces pollutant emissions

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Directory of Open Access Journals (Sweden)

Rahul Deb Das

2016-11-01

Full Text Available Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

Power and mass optimization of the hybrid solar panel and thermoelectric generators

International Nuclear Information System (INIS)

Kwan, Trevor Hocksun; Wu, Xiaofeng

2016-01-01

Highlights: • The dynamics of the hybrid PV/TEG system operating in outer space is studied. • A generalized thermodynamic model of the hybrid PV/TEG system is given. • This model is then simplified to consider the outer space scenario. • The design of the hybrid PV/TEG system is optimized using the NSGA-II algorithm. • The optimized hybrid system is more efficient than its monolithic counterparts. - Abstract: The thermoelectric generator (TEG) has been widely considered as an electrical power source in many ground applications because of its clean and noiseless characteristics. Moreover, the hybrid photovoltaic cell and TEG (PV/TEG) system has also received wide attention due to its improved power conversion efficiency over its monolithic counterparts. This paper presents a study of the dynamics and the operation of the hybrid PV/TEG system in an outer space environment where a unified thermodynamic model of this system is presented. Moreover, the multi-objective NSGA-II genetic algorithm is utilized to optimize the design of the TEG both in terms of optimal output power and in terms of mass. Specifically, the design of the single stage and the two stage variant of the aforementioned TEG are considered. Simulation results indicate that the optimized PV/TEG system does indeed achieve better efficiencies than that of the monolithic counterparts. Furthermore, it is shown that the single stage TEG is more beneficial than the two stage TEG in terms of achieving optimal performance.

MEAT: An Authoring Tool for Generating Adaptable Learning Resources

Science.gov (United States)

Kuo, Yen-Hung; Huang, Yueh-Min

2009-01-01

Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

Generating Multimedia Components for M-Learning

Directory of Open Access Journals (Sweden)

Adriana REVEIU

2009-01-01

Full Text Available The paper proposes a solution to generate template based multimedia components for instruction and learning available both for computer based applications and for mobile devices. The field of research is situated at the intersection of computer science, mobile tools and e-learning and is generically named mobile learning or M-learning. The research goal is to provide access to computer based training resources from any location and to adapt the training content to the specific features of mobile devices, communication environment, users' preferences and users' knowledge. To become important tools in education field, the technical solutions proposed will follow to use the potential of mobile devices.

Generation of Tutorial Dialogues: Discourse Strategies for Active Learning

Science.gov (United States)

1998-05-29

AND SUBTITLE Generation of Tutorial Dialogues: Discourse Strategies for active Learning AUTHORS Dr. Martha Evens 7. PERFORMING ORGANI2ATION NAME...time the student starts in on a new topic. Michael and Rovick constantly attempt to promote active learning . They regularly use hints and only resort...Controlling active learning : How tutors decide when to generate hints. Proceedings of FLAIRS 󈨣. Melbourne Beach, FL. 157-161. Hume, G., Michael

Paintings discrimination by mice: Different strategies for different paintings.

Science.gov (United States)

Watanabe, Shigeru

2017-09-01

C57BL/6 mice were trained on simultaneous discrimination of paintings with multiple exemplars, using an operant chamber with a touch screen. The number of exemplars was successively increased up to six. Those mice trained in Kandinsky/Mondrian discrimination showed improved learning and generalization, whereas those trained in Picasso/Renoir discrimination showed no improvements in learning or generalization. These results suggest category-like discrimination in the Kandinsky/Mondrian task, but item-to-item discrimination in the Picasso/Renoir task. Mice maintained their discriminative behavior in a pixelization test with various paintings; however, mice in the Picasso/Renoir task showed poor performance in a test that employed scrambling processing. These results do not indicate that discrimination strategy for any Kandinsky/Mondrian combinations differed from that for any Picasso/Monet combinations but suggest the mice employed different strategies of discrimination tasks depending upon stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

DC Linked Hybrid Generation System with an Energy Storage Device including a Photo-Voltaic Generation and a Gas Engine Cogeneration for Residential Houses

Science.gov (United States)

Lung, Chienru; Miyake, Shota; Kakigano, Hiroaki; Miura, Yushi; Ise, Toshifumi; Momose, Toshinari; Hayakawa, Hideki

For the past few years, a hybrid generation system including solar panel and gas cogeneration is being used for residential houses. Solar panels can generate electronic power at daytime; meanwhile, it cannot generate electronic power at night time. But the power consumption of residential houses usually peaks in the evening. The gas engine cogeneration system can generate electronic power without such a restriction, and it also can generate heat power to warm up house or to produce hot water. In this paper, we propose the solar panel and gas engine co-generation hybrid system with an energy storage device that is combined by dc bus. If a black out occurs, the system still can supply electronic power for special house loads. We propose the control scheme for the system which are related with the charging level of the energy storage device, the voltage of the utility grid which can be applied both grid connected and stand alone operation. Finally, we carried out some experiments to demonstrate the system operation and calculation for loss estimation.

Comparing Hybrid Learning with Traditional Approaches on Learning the Microsoft Office Power Point 2003 Program in Tertiary Education

Science.gov (United States)

Vernadakis, Nikolaos; Antoniou, Panagiotis; Giannousi, Maria; Zetou, Eleni; Kioumourtzoglou, Efthimis

2011-01-01

The purpose of this study was to determine the effectiveness of a hybrid learning approach to deliver a computer science course concerning the Microsoft office PowerPoint 2003 program in comparison to delivering the same course content in the form of traditional lectures. A hundred and seventy-two first year university students were randomly…

Effects of X-ray radiation on complex visual discrimination learning and social recognition memory in rats.

Directory of Open Access Journals (Sweden)

Catherine M Davis

Full Text Available The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal. One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min, while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation.

Effects of X-ray radiation on complex visual discrimination learning and social recognition memory in rats.

Science.gov (United States)

Davis, Catherine M; Roma, Peter G; Armour, Elwood; Gooden, Virginia L; Brady, Joseph V; Weed, Michael R; Hienz, Robert D

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation.

Effects of X-Ray Radiation on Complex Visual Discrimination Learning and Social Recognition Memory in Rats

Science.gov (United States)

Davis, Catherine M.; Roma, Peter G.; Armour, Elwood; Gooden, Virginia L.; Brady, Joseph V.; Weed, Michael R.; Hienz, Robert D.

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation. PMID:25099152

Student-generated e-learning for clinical education.

Science.gov (United States)

Isaacs, Alex N; Nisly, Sarah; Walton, Alison

2017-04-01

Within clinical education, e-learning facilitates a standardised learning experience to augment the clinical experience while enabling learner and teacher flexibility. With the shift of students from consumers to creators, student-generated content is expanding within higher education; however, there is sparse literature evaluating the impact of student-developed e-learning within clinical education. The aim of this study was to implement and evaluate a student-developed e-learning clinical module series within ambulatory care clinical pharmacy experiences. Three clinical e-learning modules were developed by students for use prior to clinical experiences. E-learning modules were created by fourth-year professional pharmacy students and reviewed by pharmacy faculty members. A pre-/post-assessment was performed to evaluate knowledge comprehension before and after participating in the e-learning modules. Additionally, a survey on student perceptions of this educational tool was performed at the end of the clinical experience. There is sparse literature evaluating the impact of student-developed e-learning within clinical education RESULTS: Of the 31 students eligible for study inclusion, 94 per cent participated in both the pre- and post-assessments. The combined post-assessment score was significantly improved after participating in the student-developed e-learning modules (p = 0.008). The student perception survey demonstrated positive perceptions of e-learning within clinical education. Student-generated e-learning was able to enhance knowledge and was positively perceived by learners. As e-learning continues to expand within health sciences education, students can be incorporated into the development and execution of this educational tool. © 2016 John Wiley & Sons Ltd.

Semi-supervised Learning with Deep Generative Models

NARCIS (Netherlands)

Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.

2014-01-01

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

A Hybrid Estimator for Active/Reactive Power Control of Single-Phase Distributed Generation Systems with Energy Storage

DEFF Research Database (Denmark)

Pahlevani, Majid; Eren, Suzan; Guerrero, Josep M.

2016-01-01

This paper presents a new active/reactive power closed-loop control system for a hybrid renewable energy generation system used for single-phase residential/commercial applications. The proposed active/reactive control method includes a hybrid estimator, which is able to quickly and accurately es...

Generation of suprathermal electrons during plasma current startup by lower hybrid waves in a tokamak

International Nuclear Information System (INIS)

Ohkubo, K.; Toi, K.; Kawahata, K.

1984-10-01

Suprathermal electrons which carry a seed current are generated by non-resonant parametric decay instability during initial phase of lower hybrid current startup in the JIPP T-IIU tokamak. From the numerical analysis, it is found that parametrically excited lower hybrid waves at lower side band can bridge the spectral gap between the thermal velocity and the low velocity end in the pump power spectrum. (author)

A Hybrid Feature Subset Selection Algorithm for Analysis of High Correlation Proteomic Data

Science.gov (United States)

Kordy, Hussain Montazery; Baygi, Mohammad Hossein Miran; Moradi, Mohammad Hassan

2012-01-01

Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power. PMID:23717808

Hybrid Test Bed of Wind Electric Generator with Photovoltaic Panels

OpenAIRE

G.D.Anbarasi Jebaselvi; S.Paramasivam

2014-01-01

Driven by the increasing costs of power production and decreasing fossil fuel reserves with the addition of global environmental concerns, renewable energy is now becoming significant fraction of total electricity production in the world. Advancements in the field of wind electric generator technology and power electronics help to achieve rapid progress in hybrid power system which mainly involves wind, solar and diesel energy with a good battery back-up. Here the discussion brings about the ...

Modeling and performance analysis of a concentrated photovoltaic–thermoelectric hybrid power generation system

International Nuclear Information System (INIS)

Lamba, Ravita; Kaushik, S.C.

2016-01-01

Highlights: • Thermodynamic model of concentrated photovoltaic–thermoelectric system is analysed. • Thomson effect reduces the power output of PV, TE and hybrid PV–TEG system. • Effect of thermocouple number, irradiance, PV and TE current have been studied. • The optimum concentration ratio for maximum power output has been found out. • The overall efficiency and power output of hybrid PV–TEG system has been improved. - Abstract: In this study, a thermodynamic model for analysing the performance of a concentrated photovoltaic–thermoelectric generator (CPV–TEG) hybrid system including Thomson effect in conjunction with Seebeck, Joule and Fourier heat conduction effects has been developed and simulated in MATALB environment. The expressions for calculating the temperature of photovoltaic (PV) module, hot and cold sides of thermoelectric (TE) module are derived analytically as well. The effect of concentration ratio, number of thermocouples in TE module, solar irradiance, PV module current and TE module current on power output and efficiency of the PV, TEG and hybrid PV–TEG system have been studied. The optimum concentration ratio corresponding to maximum power output of the hybrid system has been found out. It has been observed that by considering Thomson effect in TEG module, the power output of the PV, TE and hybrid PV–TEG systems decreases and at C = 1 and 5, it reduces the power output of hybrid system by 0.7% and 4.78% respectively. The results of this study may provide basis for performance optimization of a practical irreversible CPV–TEG hybrid system.

Modeling And Simulation As The Basis For Hybridity In The Graphic Discipline Learning/Teaching Area

Directory of Open Access Journals (Sweden)

Jana Žiljak Vujić

2009-01-01

Full Text Available Only some fifteen years have passed since the scientific graphics discipline was established. In the transition period from the College of Graphics to «Integrated Graphic Technology Studies» to the contemporary Faculty of Graphics Arts with the University in Zagreb, three main periods of development can be noted: digital printing, computer prepress and automatic procedures in postpress packaging production. Computer technology has enabled a change in the methodology of teaching graphics technology and studying it on the level of secondary and higher education. The task has been set to create tools for simulating printing processes in order to master the program through a hybrid system consisting of methods that are separate in relation to one another: learning with the help of digital models and checking in the actual real system. We are setting a hybrid project for teaching because the overall acquired knowledge is the result of completely different methods. The first method is on the free programs level functioning without consequences. Everything remains as a record in the knowledge database that can be analyzed, statistically processed and repeated with new parameter values of the system being researched. The second method uses the actual real system where the results are in proving the value of new knowledge and this is something that encourages and stimulates new cycles of hybrid behavior in mastering programs. This is the area where individual learning incurs. The hybrid method allows the possibility of studying actual situations on a computer model, proving it on an actual real model and entering the area of learning envisaging future development.

Modeling and Simulation as the Basis for Hybridity in the Graphic Discipline Learning/Teaching Area

Directory of Open Access Journals (Sweden)

Vilko Ziljak

2009-11-01

Full Text Available Only some fifteen years have passed since the scientific graphics discipline was established. In the transition period from the College of Graphics to «Integrated Graphic Technology Studies» to the contemporary Faculty of Graphics Arts with the University in Zagreb, three main periods of development can be noted: digital printing, computer prepress and automatic procedures in postpress packaging production. Computer technology has enabled a change in the methodology of teaching graphics technology and studying it on the level of secondary and higher education. The task has been set to create tools for simulating printing processes in order to master the program through a hybrid system consisting of methods that are separate in relation to one another: learning with the help of digital models and checking in the actual real system.  We are setting a hybrid project for teaching because the overall acquired knowledge is the result of completely different methods. The first method is on the free programs level functioning without consequences. Everything remains as a record in the knowledge database that can be analyzed, statistically processed and repeated with new parameter values of the system being researched. The second method uses the actual real system where the results are in proving the value of new knowledge and this is something that encourages and stimulates new cycles of hybrid behavior in mastering programs. This is the area where individual learning incurs. The hybrid method allows the possibility of studying actual situations on a computer model, proving it on an actual real model and entering the area of learning envisaging future development.

The doubly conditioned frequency spectrum does not distinguish between ancient population structure and hybridization

KAUST Repository

Eriksson, Anders

2014-03-13

Distinguishing between hybridization and population structure in the ancestral species is a key challenge in our understanding of how permeable species boundaries are to gene flow. The doubly conditioned frequency spectrum (dcfs) has been argued to be a powerful metric to discriminate between these two explanations, and it was used to argue for hybridization between Neandertal and anatomically modern humans. The shape of the observed dcfs for these two species cannot be reproduced by a model that represents ancient population structure in Africa with two populations, while adding hybridization produces realistic shapes. In this letter, we show that this result is a consequence of the spatial coarseness of the demographic model and that a spatially structured stepping stone model can generate realistic dcfs without hybridization. This result highlights how inferences on hybridization between recently diverged species can be strongly affected by the choice of how population structure is represented in the underlying demographic model. We also conclude that the dcfs has limited power in distinguishing between the signals left by hybridization and ancient structure. 2014 The Author.

The doubly conditioned frequency spectrum does not distinguish between ancient population structure and hybridization

KAUST Repository

Eriksson, Anders; Manica, Andrea

2014-01-01

Distinguishing between hybridization and population structure in the ancestral species is a key challenge in our understanding of how permeable species boundaries are to gene flow. The doubly conditioned frequency spectrum (dcfs) has been argued to be a powerful metric to discriminate between these two explanations, and it was used to argue for hybridization between Neandertal and anatomically modern humans. The shape of the observed dcfs for these two species cannot be reproduced by a model that represents ancient population structure in Africa with two populations, while adding hybridization produces realistic shapes. In this letter, we show that this result is a consequence of the spatial coarseness of the demographic model and that a spatially structured stepping stone model can generate realistic dcfs without hybridization. This result highlights how inferences on hybridization between recently diverged species can be strongly affected by the choice of how population structure is represented in the underlying demographic model. We also conclude that the dcfs has limited power in distinguishing between the signals left by hybridization and ancient structure. 2014 The Author.

DLNE: A hybridization of deep learning and neuroevolution for visual control

DEFF Research Database (Denmark)

Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

2017-01-01

This paper investigates the potential of combining deep learning and neuroevolution to create a bot for a simple first person shooter (FPS) game capable of aiming and shooting based on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition...... on evolution, and (3) how well they allow the deep network and evolved network to interface with each other. Overall, the results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks...... and translating raw pixels to compact feature representations, while the evolving network takes those features as inputs to infer actions. Two types of feature representations are evaluated in terms of (1) how precise they allow the deep network to recognize the position of the enemy, (2) their effect...

Learning features for tissue classification with the classification restricted Boltzmann machine

DEFF Research Database (Denmark)

van Tulder, Gijs; de Bruijne, Marleen

2014-01-01

Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convo...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

Science.gov (United States)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Mode of inheritance for fruit firmness in tomato hybrids of F1 generation (Lycoperscum esculentum Mill.

Directory of Open Access Journals (Sweden)

Sušić Zoran

2000-01-01

Full Text Available Present day program for tomato selection are aimed at creating the genotypes with firm fruit. The fruits with this quality surfer from minor injuries while being harvested and transported, which directly affects their better consumption purpose. By crossing seven divergent tomato genotypes that differed among themselves in fruit firmness, and by applying the method of full diallel without reciprocal crossings, we obtained 21 hybrids of F1 generation. Upon analyzing the components of the genetic variance we found out that dominant genes prevailed in inheriting this feature. Considering all the crossing combinations together, it could be concluded that super dominance was the mode of inheritance recorded in Fl generation. The hybrid combination obtained by crossing the two hybrids with the best general combining ability (V-100 x No-10 was characterized by the best specific combining ability. .

Small-Signal Analysis of Autonomous Hybrid Distributed Generation Systems in Presence of Ultracapacitor and Tie-Line Operation

Science.gov (United States)

Ray, Prakash K.; Mohanty, Soumya R.; Kishor, Nand

2010-07-01

This paper presents small-signal analysis of isolated as well as interconnected autonomous hybrid distributed generation system for sudden variation in load demand, wind speed and solar radiation. The hybrid systems comprise of different renewable energy resources such as wind, photovoltaic (PV) fuel cell (FC) and diesel engine generator (DEG) along with the energy storage devices such as flywheel energy storage system (FESS) and battery energy storage system (BESS). Further ultracapacitors (UC) as an alternative energy storage element and interconnection of hybrid systems through tie-line is incorporated into the system for improved performance. A comparative assessment of deviation of frequency profile for different hybrid systems in the presence of different storage system combinations is carried out graphically as well as in terms of the performance index (PI), ie integral square error (ISE). Both qualitative and quantitative analysis reflects the improvements of the deviation in frequency profiles in the presence of the ultracapacitors (UC) as compared to other energy storage elements.

Inheritance of carbon isotope discrimination and water-use efficiency in cowpea

International Nuclear Information System (INIS)

Ismail, A.M.; Hall, A.E.

1993-01-01

Theory has been developed predicting an association between water-use efficiency (WUE = total biomass/transpiration) and leaf discrimination against 13C carbon isotope discrimination which could be used to indirectly select for WUE in C3 plants. Previous studies indicated variation in WUE and carbon isotope discrimination among genotypes of cowpea [Vigna unguiculata (L.) Walp.] and due to drought. Moreover, a highly significant negative correlation between WUE and carbon isotope discrimination was observed for both genotypic and drought effects, as expected based on theory. Present studies were conducted to investigate whether the inheritance of WUE and carbon isotope discrimination is nuclear or maternal, and whether any dominance is present. Contrasting cowpea accessions and hybrids were grown over 2 yr in two outdoor pot experiments, subjected to wet or dry treatments, and under full irrigation in natural soil conditions in 1 yr. Highly significant differences in WUE were observed among cowpea parents and hybrids, and due to drought, which were strongly and negatively correlated with carbon isotope discrimination as expected based on theory. Data from reciprocal crosses indicated that both WUE and carbon isotope discrimination are controlled by nuclear genes. High WUE and low carbon isotope discrimination exhibited partial dominance under pot conditions. In contrast, high carbon isotope discrimination was partially dominant for plants grown under natural soil conditions but in a similar aerial environment as in the pot studies. We speculate that differences in rooting conditions were responsible for the differences in extent of dominance for carbon isotope discrimination of plants growing under pot conditions compared with natural soil conditions in a similar field aerial environment

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease

Science.gov (United States)

Voigt, Ingmar; Vitanovski, Dime; Ionasec, Razvan I.; Tsymal, Alexey; Georgescu, Bogdan; Zhou, Shaohua K.; Huber, Martin; Navab, Nassir; Hornegger, Joachim; Comaniciu, Dorin

2010-03-01

Disorders of the heart valves constitute a considerable health problem and often require surgical intervention. Recently various approaches were published seeking to overcome the shortcomings of current clinical practice,that still relies on manually performed measurements for performance assessment. Clinical decisions are still based on generic information from clinical guidelines and publications and personal experience of clinicians. We present a framework for retrieval and decision support using learning based discriminative distance functions and visualization of patient similarity with relative neighborhood graphsbased on shape and derived features. We considered two learning based techniques, namely learning from equivalence constraints and the intrinsic Random Forest distance. The generic approach enables for learning arbitrary user-defined concepts of similarity depending on the application. This is demonstrated with the proposed applications, including automated diagnosis and interventional suitability classification, where classification rates of up to 88.9% and 85.9% could be observed on a set of valve models from 288 and 102 patients respectively.

Perceptual Learning: 12-Month-Olds' Discrimination of Monkey Faces

Science.gov (United States)

Fair, Joseph; Flom, Ross; Jones, Jacob; Martin, Justin

2012-01-01

Six-month-olds reliably discriminate different monkey and human faces whereas 9-month-olds only discriminate different human faces. It is often falsely assumed that perceptual narrowing reflects a permanent change in perceptual abilities. In 3 experiments, ninety-six 12-month-olds' discrimination of unfamiliar monkey faces was examined. Following…

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

Science.gov (United States)

Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

Age-related sensitive periods influence visual language discrimination in adults.

Science.gov (United States)

Weikum, Whitney M; Vouloumanos, Athena; Navarra, Jordi; Soto-Faraco, Salvador; Sebastián-Gallés, Núria; Werker, Janet F

2013-01-01

Adults as well as infants have the capacity to discriminate languages based on visual speech alone. Here, we investigated whether adults' ability to discriminate languages based on visual speech cues is influenced by the age of language acquisition. Adult participants who had all learned English (as a first or second language) but did not speak French were shown faces of bilingual (French/English) speakers silently reciting sentences in either language. Using only visual speech information, adults who had learned English from birth or as a second language before the age of 6 could discriminate between French and English significantly better than chance. However, adults who had learned English as a second language after age 6 failed to discriminate these two languages, suggesting that early childhood exposure is crucial for using relevant visual speech information to separate languages visually. These findings raise the possibility that lowered sensitivity to non-native visual speech cues may contribute to the difficulties encountered when learning a new language in adulthood.

An in-depth assessment of hybrid solar–geothermal power generation

International Nuclear Information System (INIS)

Zhou, Cheng; Doroodchi, Elham; Moghtaderi, Behdad

2013-01-01

Highlights: • We model hybrid solar thermal and geothermal energy conversion system in the Australian context. • Solar thermal and geothermal energy can be effectively hybridised. • Thermodynamic advantages and economic benefits are realised. • Hybrid system overcomes adverse effects of diurnal temperature change on power generation. • Cost of electricity of an Enhanced Geothermal System can drop by more than 20% if hybridised with solar energy. - Abstract: A major problem faced by many standalone geothermal power plants, particularly in hot and arid climates such as Australia, is the adverse effects of diurnal temperature change on the operation of air-cooled condensers which typically leads to fluctuation in the power output and degradation of thermal efficiency. This study is concerned with the assessment of hybrid solar–geothermal power plants as a means of boosting the power output and where possible moderating the impact of diurnal temperature change. The ultimate goal is to explore the potential benefits from the synergies between the solar and geothermal energy sources. For this purpose the performances of the hybrid systems in terms of power output and the cost of electricity were compared with that of stand-alone solar and geothermal plants. Moreover, the influence of various controlling parameters including the ambient temperature, solar irradiance, geographical location, resource quality, and the operating mode of the power cycle on the performance of the hybrid system were investigated under steady-state conditions. Unsteady-state case studies were also performed to examine the dynamic behaviour of hybrid systems. These case studies were carried out for three different Australian geographic locations using raw hourly meteorological data of a typical year. The process simulation package Aspen-HYSYS was used to simulate plant configurations of interest. Thermodynamic analyses carried out for a reservoir temperature of 120 °C and a fixed

An in-depth assessment of hybrid solar–geothermal power generation

Energy Technology Data Exchange (ETDEWEB)

Zhou, Cheng [Priority Research Centre for Energy, Discipline of Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308 (Australia); Doroodchi, Elham [Priority Research Centre for Advanced Particle Processing and Transport, Discipline of Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308 (Australia); Moghtaderi, Behdad [Priority Research Centre for Energy, Discipline of Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308 (Australia)

2013-10-15

Highlights: • We model hybrid solar thermal and geothermal energy conversion system in the Australian context. • Solar thermal and geothermal energy can be effectively hybridised. • Thermodynamic advantages and economic benefits are realised. • Hybrid system overcomes adverse effects of diurnal temperature change on power generation. • Cost of electricity of an Enhanced Geothermal System can drop by more than 20% if hybridised with solar energy. - Abstract: A major problem faced by many standalone geothermal power plants, particularly in hot and arid climates such as Australia, is the adverse effects of diurnal temperature change on the operation of air-cooled condensers which typically leads to fluctuation in the power output and degradation of thermal efficiency. This study is concerned with the assessment of hybrid solar–geothermal power plants as a means of boosting the power output and where possible moderating the impact of diurnal temperature change. The ultimate goal is to explore the potential benefits from the synergies between the solar and geothermal energy sources. For this purpose the performances of the hybrid systems in terms of power output and the cost of electricity were compared with that of stand-alone solar and geothermal plants. Moreover, the influence of various controlling parameters including the ambient temperature, solar irradiance, geographical location, resource quality, and the operating mode of the power cycle on the performance of the hybrid system were investigated under steady-state conditions. Unsteady-state case studies were also performed to examine the dynamic behaviour of hybrid systems. These case studies were carried out for three different Australian geographic locations using raw hourly meteorological data of a typical year. The process simulation package Aspen-HYSYS was used to simulate plant configurations of interest. Thermodynamic analyses carried out for a reservoir temperature of 120 °C and a fixed

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Discriminating Children with Autism from Children with Learning Difficulties with an Adaptation of the Short Sensory Profile

Science.gov (United States)

O'Brien, Justin; Tsermentseli, Stella; Cummins, Omar; Happe, Francesca; Heaton, Pamela; Spencer, Janine

2009-01-01

In this article, we examine the extent to which children with autism and children with learning difficulties can be discriminated from their responses to different patterns of sensory stimuli. Using an adapted version of the Short Sensory Profile (SSP), sensory processing was compared in 34 children with autism to 33 children with typical…

Thermoelectric Power Generation System for Future Hybrid Vehicles Using Hot Exhaust Gas

Science.gov (United States)

Kim, Sun-Kook; Won, Byeong-Cheol; Rhi, Seok-Ho; Kim, Shi-Ho; Yoo, Jeong-Ho; Jang, Ju-Chan

2011-05-01

The present experimental and computational study investigates a new exhaust gas waste heat recovery system for hybrid vehicles, using a thermoelectric module (TEM) and heat pipes to produce electric power. It proposes a new thermoelectric generation (TEG) system, working with heat pipes to produce electricity from a limited hot surface area. The current TEG system is directly connected to the exhaust pipe, and the amount of electricity generated by the TEMs is directly proportional to their heated area. Current exhaust pipes fail to offer a sufficiently large hot surface area for the high-efficiency waste heat recovery required. To overcome this, a new TEG system has been designed to have an enlarged hot surface area by the addition of ten heat pipes, which act as highly efficient heat transfer devices and can transmit the heat to many TEMs. As designed, this new waste heat recovery system produces a maximum 350 W when the hot exhaust gas heats the evaporator surface of the heat pipe to 170°C; this promises great possibilities for application of this technology in future energy-efficient hybrid vehicles.

Students' satisfaction to hybrid problem-based learning format for basic life support/advanced cardiac life support teaching.

Science.gov (United States)

Chilkoti, Geetanjali; Mohta, Medha; Wadhwa, Rachna; Saxena, Ashok Kumar; Sharma, Chhavi Sarabpreet; Shankar, Neelima

2016-11-01

Students are exposed to basic life support (BLS) and advanced cardiac life support (ACLS) training in the first semester in some medical colleges. The aim of this study was to compare students' satisfaction between lecture-based traditional method and hybrid problem-based learning (PBL) in BLS/ACLS teaching to undergraduate medical students. We conducted a questionnaire-based, cross-sectional survey among 118 1 st -year medical students from a university medical college in the city of New Delhi, India. We aimed to assess the students' satisfaction between lecture-based and hybrid-PBL method in BLS/ACLS teaching. Likert 5-point scale was used to assess students' satisfaction levels between the two teaching methods. Data were collected and scores regarding the students' satisfaction levels between these two teaching methods were analysed using a two-sided paired t -test. Most students preferred hybrid-PBL format over traditional lecture-based method in the following four aspects; learning and understanding, interest and motivation, training of personal abilities and being confident and satisfied with the teaching method ( P < 0.05). Implementation of hybrid-PBL format along with the lecture-based method in BLS/ACLS teaching provided high satisfaction among undergraduate medical students.

Students' perception towards the problem based learning tutorial session in a system-based hybrid curriculum.

Science.gov (United States)

Al-Drees, Abdulmajeed A; Khalil, Mahmoud S; Irshad, Mohammad; Abdulghani, Hamza M

2015-03-01

To evaluate students' perception towards the problem based learning (PBL) session in a system-based hybrid curriculum. We conducted a cross-sectional study in the College of Medicine, King Saud University, Saudi Arabia at the end of the 2012-2013 academic year. The survey questionnaire was self-administered, and examined perceptions of PBL session benefits, appropriate running of sessions, and tutor's roles. Out of 510 students, 275 (53.9%) completed the questionnaire. Most of the students reported that PBL sessions were helpful in understanding basic sciences concepts (p=0.04). In addition, they agreed that PBL sessions increased their knowledge of basic sciences (p=0.01). Most students reported that PBL sessions encouraged self-directed learning, collaborative learning, and improved decision making skills. However, 54.5% of students reported lack of proper training before starting the PBL sessions, and only 25.1% of students agreed that the teaching staff are well prepared to run the sessions. Most students used the internet (93.1%), lecture notes (76.7%), and books (64.4%) as learning resources. Most students reported repetition of topics between PBL sessions and lectures (p=0.07). The study highlighted the significant role of PBL in a system-based hybrid curriculum and helped students improve their knowledge and different learning skills. Students and staff training is required before the utilizing the PBL as an instructional method.

Data preprocessing techniques for classification without discrimination

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.

2012-01-01

Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have

Hippocampal theta activity is selectively associated with contingency detection but not discrimination in rabbit discrimination-reversal eyeblink conditioning.

Science.gov (United States)

Nokia, Miriam S; Wikgren, Jan

2010-04-01

The relative power of the hippocampal theta-band ( approximately 6 Hz) activity (theta ratio) is thought to reflect a distinct neural state and has been shown to affect learning rate in classical eyeblink conditioning in rabbits. We sought to determine if the theta ratio is mostly related to the detection of the contingency between the stimuli used in conditioning or also to the learning of more complex inhibitory associations when a highly demanding delay discrimination-reversal eyeblink conditioning paradigm is used. A high hippocampal theta ratio was not only associated with a fast increase in conditioned responding in general but also correlated with slow emergence of discriminative responding due to sustained responding to the conditioned stimulus not paired with an unconditioned stimulus. The results indicate that the neural state reflected by the hippocampal theta ratio is specifically linked to forming associations between stimuli rather than to the learning of inhibitory associations needed for successful discrimination. This is in line with the view that the hippocampus is responsible for contingency detection in the early phase of learning in eyeblink conditioning. (c) 2009 Wiley-Liss, Inc.

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Directory of Open Access Journals (Sweden)

Laszlo Papp

2018-06-01

Full Text Available Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual- or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic, also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid

Generation to generation: discrimination and harassment experiences of physician mothers and their physician daughters.

Science.gov (United States)

Shrier, Diane K; Zucker, Alyssa N; Mercurio, Andrea E; Landry, Laura J; Rich, Michael; Shrier, Lydia A

2007-01-01

To examine bias and sexual harassment experiences of physician mothers and their physician daughters; correlations of these experiences with career satisfaction, stress at work, stress at home, and percentage of women in specialty; and influences of the mother on her daughter's experiences. A convenience sample of 214 families with mother and daughter physicians was sent a 56-item survey that included questions on bias and sexual harassment experiences. Statistical comparisons were made within 136 dyads where both mother and daughter returned the questionnaire. Eighty-four percent of mothers and 87% of daughters responded. Mothers and daughters reported similarly high rates and severity of sexual harassment before medical school, while in residency/fellowship, while in practice/work setting, and by teachers and supervisors. Daughters reported higher rates of harassment during medical school and by patients, mothers by colleagues. Gender and racial/ethnic discrimination was lower for daughters compared with their mothers, but gender discrimination was still substantial. Compared with other daughters, daughters who experienced discrimination or sexual harassment reported lower career satisfaction and more stress at work and at home and worked in specialties with fewer women. Gender discrimination and sexual harassment remain entrenched in medical education and professional workplaces. Maternal role models and mentors were not as protective as anticipated. Leadership of medical institutions and professional associations must deal more effectively with persistent discrimination and harassment or risk the loss of future leaders.

Hybrid Systems of Distributed Generation with Renewable Sources: Modeling and Analysis of Their Operational Modes in Electric Power System

Directory of Open Access Journals (Sweden)

A. M. Gashimov

2013-01-01

Full Text Available The paper considers problems pertaining to modeling and simulation of operational hybrid system modes of the distributed generation comprising conventional sources – modular diesel generators, gas-turbine power units; and renewable sources – wind and solar power plants. Operational modes of the hybrid system have been investigated under conditions of electrical connection with electric power system and in case of its isolated operation. As a consequence

Assessing the Effectiveness of a Hybrid-Flipped Model of Learning on Fluid Mechanics Instruction: Overall Course Performance, Homework, and Far- and Near-Transfer of Learning

Science.gov (United States)

Harrison, David J.; Saito, Laurel; Markee, Nancy; Herzog, Serge

2017-01-01

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity…

Net Generation's Learning Styles in Nursing Education.

Science.gov (United States)

Christodoulou, Eleni; Kalokairinou, Athina

2015-01-01

Numerous surveys have confirmed that emerging technologies and Web 2.0 tools have been a defining feature in the lives of current students, estimating that there is a fundamental shift in the way young people communicate, socialize and learn. Nursing students in higher education are characterized as digital literate with distinct traits which influence their learning styles. Millennials exhibit distinct learning preferences such as teamwork, experiential activities, structure, instant feedback and technology integration. Higher education institutions should be aware of the implications of the Net Generation coming to university and be prepared to meet their expectations and learning needs.

Development of an Optimal Power Control Scheme for Wave-Offshore Hybrid Generation Systems

Directory of Open Access Journals (Sweden)

Seungmin Jung

2015-08-01

Full Text Available Integration technology of various distribution systems for improving renewable energy utilization has been receiving attention in the power system industry. The wave-offshore hybrid generation system (HGS, which has a capacity of over 10 MW, was recently developed by adopting several voltage source converters (VSC, while a control method for adopted power conversion systems has not yet been configured in spite of the unique system characteristics of the designated structure. This paper deals with a reactive power assignment method for the developed hybrid system to improve the power transfer efficiency of the entire system. Through the development and application processes for an optimization algorithm utilizing the real-time active power profiles of each generator, a feasibility confirmation of power transmission loss reduction was implemented. To find the practical effect of the proposed control scheme, the real system information regarding the demonstration process was applied from case studies. Also, an evaluation for the loss of the improvement rate was calculated.

Performance analysis of different ORC configurations for thermal energy and LNG cold energy hybrid power generation system

Science.gov (United States)

Sun, Zhixin; Wang, Feng; Wang, Shujia; Xu, Fuquan; Lin, Kui

2017-01-01

This paper presents a thermal energy and Liquefied natural gas (LNG) cold energy hybrid power generation system. Performances of four different Organic Rankine cycle (ORC) configurations (the basic, the regenerative, the reheat and the regenerative-reheat ORCs) are studied based on the first and the second law of thermodynamics. Dry organic fluid R245fa is selected as the typical working fluid. Parameter analysis is also conducted in this paper. The results show that regeneration could not increase the thermal efficiency of the thermal and cold energy hybrid power generation system. ORC with the reheat process could produce more specific net power output but it may also reduce the system thermal efficiency. The basic and the regenerative ORCs produce higher thermal efficiency while the regenerative-reheat ORC performs best in the exergy efficiency. A preheater is necessary for the thermal and cold energy hybrid power generation system. And due to the presence of the preheater, there will be a step change of the system performance as the turbine inlet pressure rises.

Intelligent monitoring of YAG laser welding on steam generator tubes

International Nuclear Information System (INIS)

Hosaka, Shigetaka; Nagura, Yasumi; Ishide, Takashi; Nagashima, Tadashi; Akaba, Takashi

1992-01-01

The 'KASHIKOKI' intelligent device for monitoring the YAG laser welding of steam generator tubes is described in this paper. The 'KASHIKOKI', it monitors the series of six channels, for example, the reflected laser beam and the welding speed, etc. It learns the normal criteria and the anomalous criteria of welding, and discriminates between normal and anomalous welding using the learned criteria, and distinguishes the anomaly into several types. As the results of evaluation test, the degree of correspondence between this device and an expert is about 90%. This paper describes the new methods the multi-variate analysis model for discriminating between normal and anomalous welding, and a neural network model for distinguishing the types of anomaly. (author)

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Is there a digital generation gap for e-learning in plastic surgery?

Science.gov (United States)

Stevens, Roger J G; Hamilton, Neil M

2012-01-01

Some authors have claimed that those plastic surgeons born between 1965 and 1979 (generation X, or Gen-X) are more technologically able than those born between 1946 and 1964 (Baby Boomers, or BB). Those born after 1980, which comprise generation Y (Gen-Y), might be the most technologically able and most demanding for electronic learning (e-learning) to support their education and training in plastic surgery. These differences might represent a "digital generation gap" and would have practical and financial implications for the development of e-learning. The aim of this study was to survey plastic surgeons on their experience and preferences in e-learning in plastic surgery and to establish whether there was a difference between different generations. Online survey (e-survey) of plastic surgeons within the UK and Ireland was used for this study. In all, 624 plastic surgeons were invited by e-mail to complete an e-survey anonymously for their experience of e-learning in plastic surgery, whether they would like access to e-learning and, if so, whether this should this be provided nationally, locally, or not at all. By stratifying plastic surgeons into three generations (BB, Gen-X, and Gen-Y), the responses between generations were compared using the χ(2)-test for linear trend. A p value learning. These findings refute the claim that there are differences in the experience of e-learning of plastic surgeons by generation. Furthermore, there is no evidence that there are differences in whether there should be access to e-learning and how e-learning should be provided for different generations of plastic surgeons. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

International Nuclear Information System (INIS)

Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

2013-01-01

Highlights: ► We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ► The model is robust at multiple time scales with the anticipated accuracy. ► At month-scale, the SARIMA model shows good representation for monthly MSW generation. ► At medium-term time scale, grey relational analysis could yield the MSW generation. ► At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 – 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 – 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

Energy Technology Data Exchange (ETDEWEB)

Xu, Lilai, E-mail: llxu@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Gao, Peiqing, E-mail: peiqing15@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China); Cui, Shenghui, E-mail: shcui@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Liu, Chun, E-mail: xmhwlc@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China)

2013-06-15

Highlights: ► We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ► The model is robust at multiple time scales with the anticipated accuracy. ► At month-scale, the SARIMA model shows good representation for monthly MSW generation. ► At medium-term time scale, grey relational analysis could yield the MSW generation. ► At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 – 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 – 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to

A handy motion driven hybrid energy harvester: dual Halbach array based electromagnetic and triboelectric generators

International Nuclear Information System (INIS)

Salauddin, M; Park, J Y

2016-01-01

In this work, we have proposed and experimentally validated of hybrid electromagnetic and triboelectric energy harvester using dual Halbach magnets array excited by human handy motion. Hybrid electromagnetic (EM) and triboelectric (TE) generator that can deliver an output performance much higher than that of the individual energy-harvesting unit due to the combination operation of EM and TE mechanisms under the same mechanical movements. A Halbach array concentrates the magnetic flux lines on one side of the array while suppressing the flux lines on the other side. Dual Halbach array allows the concentrated magnetic flux lines to interact with the same coil in a way where maximum flux linkage occurs. When an external mechanical vibration is applied to the hybrid structure in the axial direction of the harvester, the suspended mass (two sided dual-Halbach-array frame) starts to oscillate within the magnetic springs and TEG part. Therefore, the TEG part, the Al film and microstructure PDMS film are collected into full contact with each other, generating triboelectric charges due to the various triboelectricities between them. A prototype of the hybrid harvester has been fabricated and tested. The EMG is capable of delivering maximum 11.5mW peak power at 32.5Ω matching load resistance and the TEG delivering 88μW peak power at 10MΩ load resistance. (paper)

An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

Directory of Open Access Journals (Sweden)

Jinyi Long

2017-01-01

Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

Science.gov (United States)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

KAUST Repository

Kamiran, Faisal

2012-11-18

Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train discrimination-free classifiers on such historical data that are discriminative, with respect to the given sensitive attribute. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes. For example, in a job application, the education level of a job candidate could be such an explainable attribute. If the data contain many highly educated male candidates and only few highly educated women, a difference in acceptance rates between woman and man does not necessarily reflect gender discrimination, as it could be explained by the different levels of education. Even though selecting on education level would result in more males being accepted, a difference with respect to such a criterion would not be considered to be undesirable, nor illegal. Current state-of-the-art techniques, however, do not take such gender-neutral explanations into account and tend to overreact and actually start reverse discriminating, as we will show in this paper. Therefore, we introduce and analyze the refined notion of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and are hence tolerable. Therefore, we develop methodology for quantifying the explainable discrimination and algorithmic techniques for removing the illegal discrimination when one or more attributes are considered as explanatory. Experimental evaluation on synthetic and real-world classification datasets demonstrates that the new techniques are superior to the old ones in this new context, as they succeed in

Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

Science.gov (United States)

Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

2016-01-01

An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

Engineering Hybrid Learning Communities: The Case of a Regional Parent Community

Directory of Open Access Journals (Sweden)

Sven Strickroth

2014-09-01

Full Text Available We present an approach (and a corresponding system design for supporting regionally bound hybrid learning communities (i.e., communities which combine traditional face-to-face elements with web based media such as online community platforms, e-mail and SMS newsletters. The goal of the example community used to illustrate the approach was to support and motivate (especially hard-to-reach underprivileged parents in the education of their young children. The article describes the design process used and the challenges faced during the socio-technical system design. An analysis of the community over more than one year indicates that the hybrid approach works better than the two separated “traditional” approaches separately. Synergy effects like advertising effects from the offline trainings for the online platform and vice versa occurred and regular newsletters turned out to have a noticeable effect on the community.

Intergenerational Learning Program: A Bridge between Generations

Directory of Open Access Journals (Sweden)

Seyedeh Zahra Aemmi

2017-12-01

Full Text Available One of the goals of education can be considered the transfer of knowledge, skills, competencies, wisdom, norms and values between generations. Intergenerational learning program provide this goal and opportunities for lifelong learning and sharing knowledge and experience between generations. This review aimed to investigate the benefits of this program for the children and older adult and its application in health care systems. An extensive literature search was conducted in some online databases such as Magiran, SID, Scopus, EMBASE, and Medline via PubMed until July 2016 and Persian and English language publications studied that met inclusion criteria. The review concluded that this program can be provided wonderful resources for the social and emotional growth of the children and older adults and can be used for caring, education and follow-up in health care systems especially by nurses. Also, this review highlighted the need for research about this form of learning in Iran.

Study on fission blanket fuel cycling of a fusion-fission hybrid energy generation system

International Nuclear Information System (INIS)

Zhou, Z.; Yang, Y.; Xu, H.

2011-01-01

This paper presents a preliminary study on neutron physics characteristics of a light water cooled fission blanket for a new type subcritical fusion-fission hybrid reactor aiming at electric power generation with low technical limits of fission fuel. The major objective is to study the fission fuel cycling performance in the blanket, which may possess significant impacts on the feasibility of the new concept of fusion-fission hybrid reactor with a high energy gain (M) and tritium breeding ratio (TBR). The COUPLE2 code developed by the Institute of Nuclear and New Energy Technology of Tsinghua University is employed to simulate the neutronic behaviour in the blanket. COUPLE2 combines the particle transport code MCNPX with the fuel depletion code ORIGEN2. The code calculation results show that soft neutron spectrum can yield M > 20 while maintaining TBR >1.15 and the conversion ratio of fissile materials CR > 1 in a reasonably long refuelling cycle (>five years). The preliminary results also indicate that it is rather promising to design a high-performance light water cooled fission blanket of fusion-fission hybrid reactor for electric power generation by directly loading natural or depleted uranium if an ITER-scale tokamak fusion neutron source is achievable.

Experimental study of a sustainable hybrid system for thermoelectric generation and freshwater production

Science.gov (United States)

de Souza, Gabriel Fernandes; Tan, Lippong; Singh, Baljit; Ding, Lai Chet; Date, Abhijit

2017-04-01

The paper presents a sustainable hybrid system, which is capable of generating electricity and producing freshwater from seawater using low grade heat source. This proposed system uses low grade heat that can be supplied from solar radiation, industrial waste heat or any other waste heat sources where the temperature is less than 150°C. The concept behind this system uses the Seebeck effect for thermoelectricity generation via incorporating the low boiling point of seawater under sub-atmospheric ambient pressure. A lab-test prototype of the proposed system was built and experimentally tested in RMIT University. The prototype utilised four commercial available thermoelectric generators (Bi2Te3) and a vacuum vessel to achieve the simultaneous production of electricity and freshwater. The temperature profiles, thermoelectric powers and freshwater productions were determined at several levels of salinity to study the influence of different salt concentrations. The theoretical description of system design and experimental results were analysed and discussed in detailed. The experiment results showed that 0.75W of thermoelectricity and 404g of freshwater were produced using inputs of 150W of simulated waste heat and 500g of 3% saline water. The proposed hybrid concept has demonstrated the potential to become the future sustainable system for electricity and freshwater productions.

Discrimination of transgenic soybean seeds by terahertz spectroscopy

Science.gov (United States)

Liu, Wei; Liu, Changhong; Chen, Feng; Yang, Jianbo; Zheng, Lei

2016-10-01

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

Deep learning in color: towards automated quark/gluon jet discrimination

International Nuclear Information System (INIS)

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

2017-01-01

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Hybrid Micro-Hydro Power Generation Development in Endau Rompin National Park Johor, Malaysia

Directory of Open Access Journals (Sweden)

Yusop Azli

2017-01-01

Full Text Available Micro-Hydro electrical power systems are very useful for remote area electrification which does not had supply from the national grid. On the contrary, this area has river streams with high potential for micro-hydro power generation. As such, the UTHM ECO-Hydro Team embarked on a project for erecting a micro-hydro power plant with collaboration with National Education Research Center (NERC, Johor National Park Corporation in Endau Rompin. The existing power generation in this area at present is by using diesel generator gives negative impact on finance and environment in the long run. It supplies power to several including library, offices, open laboratory, chalets and dorms.. At the moment, the micro-hydro system complements the diesel generator, thus becoming a hybrid power generation system.

Learning Orthographic Structure with Sequential Generative Neural Networks

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-01-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

WWC Review of the Report "Interactive Online Learning on Campus: Testing MOOCs and Other Platforms in Hybrid Formats in the University System of Maryland." What Works Clearinghouse Single Study Review

Science.gov (United States)

What Works Clearinghouse, 2015

2015-01-01

In the 2014 study, "Interactive Online Learning on Campus: Testing MOOCs and Other Platforms in Hybrid Formats in the University System of Maryland," researchers examined the impact of using hybrid forms of interactive online learning in seven undergraduate courses across seven universities in Maryland. Hybrid forms of interactive online…

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery

Science.gov (United States)

Ahn, Sangtae; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan

2014-12-01

Objective. We propose a new hybrid brain-computer interface (BCI) system that integrates two different EEG tasks: tactile selective attention (TSA) using a vibro-tactile stimulator on the left/right finger and motor imagery (MI) of left/right hand movement. Event-related desynchronization (ERD) from the MI task and steady-state somatosensory evoked potential (SSSEP) from the TSA task are retrieved and combined into two hybrid senses. Approach. One hybrid approach is to measure two tasks simultaneously; the features of each task are combined for testing. Another hybrid approach is to measure two tasks consecutively (TSA first and MI next) using only MI features. For comparison with the hybrid approaches, the TSA and MI tasks are measured independently. Main results. Using a total of 16 subject datasets, we analyzed the BCI classification performance for MI, TSA and two hybrid approaches in a comparative manner; we found that the consecutive hybrid approach outperformed the others, yielding about a 10% improvement in classification accuracy relative to MI alone. It is understood that TSA may play a crucial role as a prestimulus in that it helps to generate earlier ERD prior to MI and thus sustains ERD longer and to a stronger degree; this ERD may give more discriminative information than ERD in MI alone. Significance. Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

Science.gov (United States)

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Constructing ternary polyaniline-graphene-TiO{sub 2} hybrids with enhanced photoelectrochemical performance in photo-generated cathodic protection

Energy Technology Data Exchange (ETDEWEB)

Zhang, Weiwei, E-mail: vivizhg@yahoo.com [College of Material Science and Engineering, Shandong University of Science and Technology, Qingdao 266590 (China); State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, 266590 (China); Guo, Hanlin; Sun, Haiqing [College of Material Science and Engineering, Shandong University of Science and Technology, Qingdao 266590 (China); Zeng, Rongchang [College of Material Science and Engineering, Shandong University of Science and Technology, Qingdao 266590 (China); State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, 266590 (China)

2017-07-15

Highlights: • Ternary polyaniline-graphene-TiO{sub 2} hybrids were synthesized. • Flat band potential shift facilitates electron injection to the coupled metal. • Electrons and holes transfer in the hybrids promotes electron–hole separation. • Synergistic effects of the ternary components make the hybrids photo-chargeable. - Abstract: Ternary polyaniline-graphene-TiO{sub 2} nanocomposites were constructed through a stepwise synthetic route. The hybrids exhibit remarkable enhancement in photoelectrochemical performance. The transfer of photo-excited carriers in the ternary composites facilitates the photo-induced electron-hole separation. Meanwhile, the flat band potential shift of the hybrids increases the inner electric field intensity that drives the photo-excited electron migration from the composites to the coupled metal. Furthermore, the ternary hybrids were found firstly to be photo-chargeable, which shows application potentials in photo-generated cathodic protection in dark.

Techniques for discrimination-free predictive models (Chapter 12)

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.; Custers, B.H.M.; Calders, T.G.K.; Schermer, B.W.; Zarsky, T.Z.

2013-01-01

In this chapter, we give an overview of the techniques developed ourselves for constructing discrimination-free classifiers. In discrimination-free classification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be

Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics

CERN Multimedia

CERN. Geneva

2017-01-01

There is a deep connection between machine learning and jet physics - after all, jets are defined by unsupervised learning algorithms. Jet physics has been a driving force for studying modern machine learning in high energy physics. Domain specific challenges require new techniques to make full use of the algorithms. A key focus is on understanding how and what the algorithms learn. Modern machine learning techniques for jet physics are demonstrated for classification, regression, and generation. In addition to providing powerful baseline performance, we show how to train complex models directly on data and to generate sparse stacked images with non-uniform granularity.

Effects of subchronic phencyclidine (PCP treatment on social behaviors, and operant discrimination and reversal learning in C57BL/6J mice

Directory of Open Access Journals (Sweden)

Jonathan L Brigman

2009-02-01

Full Text Available Subchronic treatment with the psychotomimetic phencyclidine (PCP has been proposed as a rodent model of the negative and cognitive/executive symptoms of schizophrenia. There has, however, been a paucity of studies on this model in mice, despite the growing use of the mouse as a subject in genetic and molecular studies of schizophrenia. In the present study, we evaluated the effects of subchronic PCP treatment (5 mg/kg twice daily x 7 days, followed by 7 days withdrawal in C57BL/6J mice on 1 social behaviors using a sociability/social novelty-preference paradigm, and 2 pairwise visual discrimination and reversal learning using a touchscreen-based operant system. Results showed that mice subchronically treated with PCP made more visits to (but did not spend more time with a social stimulus relative to an inanimate one, and made more visits and spent more time investigating a novel social stimulus over a familiar one. Subchronic PCP treatment did not significantly affect behavior in either the discrimination or reversal learning tasks. These data encourage further analysis of the potential utility of mouse subchronic PCP treatment for modeling the social withdrawal component of schizophrenia. They also indicate that the treatment regimen employed was insufficient to impair our measures of discrimination and reversal learning in the C57BL/6J strain. Further work will be needed to identify alternative methods (e.g., repeated cycles of subchronic PCP treatment, use of different mouse strains that produce discrimination and/or reversal impairment, as well as other cognitive/executive measures that are sensitive to chronic PCP treatment in mice.

A novel perceptual discrimination training task: Reducing fear overgeneralization in the context of fear learning.

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Ginat-Frolich, Rivkah; Klein, Zohar; Katz, Omer; Shechner, Tomer

2017-06-01

Generalization is an adaptive learning mechanism, but it can be maladaptive when it occurs in excess. A novel perceptual discrimination training task was therefore designed to moderate fear overgeneralization. We hypothesized that improvement in basic perceptual discrimination would translate into lower fear overgeneralization in affective cues. Seventy adults completed a fear-conditioning task prior to being allocated into training or placebo groups. Predesignated geometric shape pairs were constructed for the training task. A target shape from each pair was presented. Thereafter, participants in the training group were shown both shapes and asked to identify the image that differed from the target. Placebo task participants only indicated the location of each shape on the screen. All participants then viewed new geometric pairs and indicated whether they were identical or different. Finally, participants completed a fear generalization test consisting of perceptual morphs ranging from the CS + to the CS-. Fear-conditioning was observed through physiological and behavioural measures. Furthermore, the training group performed better than the placebo group on the assessment task and exhibited decreased fear generalization in response to threat/safety cues. The findings offer evidence for the effectiveness of the novel discrimination training task, setting the stage for future research with clinical populations. Copyright © 2017 Elsevier Ltd. All rights reserved.