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Organized representations forming a computationally useful processing structure

SYNTHESE 2023年第6期202卷 175页

作者： Shea, Nicholas Univ London Inst Philosophy Sch Adv Study Senate House London WC1A 7HU England Univ Oxford Fac Philosophy Oxford OX2 6GG England

Peter Godfrey-Smith recently introduced the idea of representational 'organization'. When a collection of representations form an organized family, similar representational vehicles carry similar contents. For example, where neural firing rate represents numerosity (an analogue magnitude representation), similar firing rates represent similar numbers of items. Organization has been elided with structural representation, but the two are in fact distinct. An under-appreciated merit of representational organization is the way it facilitates computational processing. Representations from different organized families can interact, for example to perform addition. Their being organized allows them to implement a useful computation. Many of the cases where organization has seemed significant, but which fall short of structural representation, are cases where representational organization underpins a computationally useful processing structure.

关键词： Representational theory of mind Representational organization Representational structure computational processing Semantic content

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Connected component analysis integrated edge based technique for automatic vehicular license plate recognition framework

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第7期14卷 712-723页

作者： Arafat, Md Yeasir Khairuddin, Anis Salwa Mohd Paramesran, Raveendran Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur 50603 Malaysia UCSI Univ Inst Comp Sci & Digital Innovat Kuala Lumpur 56000 Malaysia

Automatic vehicle license plate recognition (AVLPR) aims at extracting the region that contains the information of vehicle license number out of an image data and then identifying the characters apart from the human intervention. This study proposed an effective AVLPR framework where detection, segmentation and recognition of various shaped license plates have been focused. For both proper visual perception and computational processing, a pre-processing technique including grey-scaling conversion combined with close arithmetic-based dilation has been defined. Both vertical and horizontal edge densities have been enumerated by kernel matrices which enable robustness in detecting various shaped and sized license plates. For better detection of candidate region, the vertical and horizontal energy mapping features combined with Gaussian smoothing filter have been used to enable detection of license plates from both high definition and lower resolution images under various illumination conditions and crowded background. For ensuring a better character segmentation rate which is the prerequisite for higher recognition rate, a blob assessment method has been defined integrated with connected component analysis. With 400 vehicle images having varying pixels, the proposed algorithm achieves 96.5, 95.6 and 94.4% accuracy, respectively, in identifying, segmenting and recognising the plate number.

关键词： traffic engineering computing character recognition image segmentation image resolution feature extraction image recognition edge detection connected component analysis integrated edge based technique automatic vehicular license plate recognition framework automatic vehicle license plate recognition vehicle license number image data human intervention effective AVLPR framework shaped license plates proper visual perception computational processing pre-processing technique grey-scaling conversion vertical edge densities horizontal edge densities sized license plates candidate region vertical energy mapping features horizontal energy mapping features lower resolution images character segmentation rate 400 vehicle images identifying recognising segmenting recognising plate number

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MODELS FOR MOLECULAR COMPUTATION - CONFORMATIONAL AUTOMATA IN THE CYTOSKELETON

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COMPUTER 1992年第11期25卷 30-39页

作者： HAMEROFF, SR DAYHOFF, JE LAHOZBELTRA, R SAMSONOVICH, AV RASMUSSEN, S UNIV MARYLAND INST SYST RESCOLL PKMD 20742 UNIV COMPLUTENSE MADRID FAC BIOL SCIDEPT APPL MATHMADRID 3SPAIN LOS ALAMOS NATL LAB CTR NONLINEAR STUDIESLOS ALAMOSNM 87544 LOS ALAMOS NATL LAB DIV THEORYLOS ALAMOSNM 87544 SANTA FE INST SANTA FECA

The structure and conformational dynamic changes that occur in cytoskeletal proteins within living cells and evidence for their participation in computational processing are described. The role of cellular automata, in which lattice subunits with discrete states interact only with nearest neighbors, in molecular computing is discussed. Simple rules governing subunit neighbor interactions can lead to complex behaviour capable of computation. It is shown that cellular automata may be implemented in the conformational relationships among neighboring protein subunits of cytoskeletal polymers including microtubules, and microtubule conformational automata networks may signal, adapt, recognize, and subserve neural-level learning

关键词： cellular automata cellular automata cellular biophysics computational processing conformational automata cytoskeleton discrete states lattice subunits learning systems living cells microtubules molecular molecular computing molecular electronics neural nets neural-level learning proteins proteins subunit neighbor interactions

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Exploring style transfer algorithms in Animation: Enhancing visual

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ENTERTAINMENT COMPUTING 2024年 49卷

作者： He, Jia Luan Vocat Tech Coll Luan 237000 Peoples R China

The necessity to computationally process human motion to produce realistic and dynamic animations is increasing with the fourth industrial revolution. Motion style transfer provides an appealing alternative to manually creating motions from start by utilizing already recorded motion data to automatically create realistic motion samples. Motion style transfer techniques have been transformed by deep learning algorithms, especially deep neural networks (DNNs). These algorithms are excellent for motion synthesis tasks because they can anticipate future motion styles. A style transfer method called CNN-BiLSTM-ATT (Convolutional Neural Network-Bidirectional Long Short-Term Memory with Attention) is put forth to analyze spatiotemporal features. The strategy tries to realistically synthesize and represent the intricacy of human motion by merging CNNs, BiLSTMs, and attention mechanisms. The difference between reference and source styles is converted to a novel motion that might include never-before-seen movements by extracting spectral intensity representations of each. A temporally sliding window filter is added to the method to do local analysis in time for the processing of heterogeneous motion, greatly improving it. As a result, the method can be used to enrich style databases by filling in missing actions and enhancing the effectiveness of earlier style transfer techniques. Through controlled user studies and quantitative trials, the effectiveness of the suggested strategy is assessed. The outcomes show a notable advancement over earlier studies, emphasizing the method's capacity to produce thorough and accurate motion sequences.

关键词： computational processing Human motion Dynamic animations Motion style transfer Deep learning algorithms

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Subaperture Stitching Surface Errors Due to Noise 11

Subaperture Stitching Surface Errors Due to Noise

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Conference on Optical Manufacturing and Testing XI

作者： Smith, Greg A. Burge, James H. Univ Arizona Coll Opt Sci Tucson AZ 85721 USA

ISBN: (纸本)9781628417418

Subaperture stitching is a popular method for extending small, subaperture interferometer measurements to cover large-aperture optics. The method is simple in that there are only two steps: 1) make multiple measurements across the surface and 2) use well-established software techniques to merge the individual measurements into one surface estimate. Because parts of the system must move between measurements, small misalignments between subapertures are unavoidable, but easily accommodated within the software. Unfortunately this process has the potential to introduce errors. In this work, we show how random noise in a circular ring of subapertures creates artifacts in low-order surface shape estimates. The magnitude of these errors depends on setup parameters such as the number of subapertures and their overlap, as well as the measurement noise within a single subaperture. Understanding the relationships between subaperture stitching configuration and surface artifacts is important when designing high-accuracy metrology systems which rely on subaperture stitching. This work will help metrology system designers incorporate subaperture stitching into error budgets and tolerances.

关键词： metrology interferometry computational processing stitching noise theory model

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Big Data Topic Modeling with Mahout for Managing Business Analysis Services 7

Big Data Topic Modeling with Mahout for Managing Business An...

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IEEE/SICE International Symposium on System Integration (SII)

作者： Romsaiyud, Walisa Siam Univ Grad Sch Informat Technol Bangkok 10160 Thailand

ISBN: (纸本)9781479969449

Topic modeling for big data provides a key opportunity to address the needs of data-driven businesses in a way to deliver genuine value to business users simplifying search and summary processes via the vast amount of information. Many businesses have already worked with Hadoop paradigm in order to rapidly apply computational processing to merge data from several operational systems and analyze large volumes of multi-structured data. In this paper, we extended the features of collapsed variational Bayesian (CVB) inference algorithm for Latent Dirichlet Allocation (LDA) to discover the hidden topical patterns through statistical regularities and eliminate noises on Hadoop framework. The approach captures the evolution of topics in a sequentially organized corpus of documents into two mainly phases, mapping and reducing phases. In the mapping phase the probabilistic on each word, in collected documents, is calculated by using collapsed space of latent variables and parameters for summarizing words in each topic, and reducing phase to utilize the various results from map phase while predicting a new topic model from a given trained models. The study conducts the experiments based on a Reuters-21578 text categorization collection corpus on Hadoop clustering with 64 nodes to improve the computationally in a more efficient and accurate approach.

关键词： Big Data belief networks business data processing distributed processing document handling inference mechanisms pattern clustering CVB Hadoop clustering Hadoop framework Hadoop paradigm LDA Mahout Reuters-21578 text categorization collection corpus big data topic modeling business analysis service management collapsed variational Bayesian inference algorithm computational processing data-driven businesses hidden topical patterns latent dirichlet allocation map phase multistructured data operational systems sequentially organized documents corpus statistical regularities Analytical models Bayes methods Data models Hidden Markov models Inference algorithms Probabilistic logic Text categorization big data Inference algorithms electronic document handling Text categorization PVB protocol business data processing Inference mechanisms Probabilistic logic Bayesian networks data models Distributed processing Bayes methods Hidden Markov models laser Doppler anemometry Anterior interventricular branch of left coronary artery

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Exploring Modeling Fidelity for computationally Efficient Seismic Performance Assessment

Exploring Modeling Fidelity for Computationally Efficient Se...

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作者： Patsialis, Dimitrios University of Notre Dame

学位级别：Ph.D., Doctor of Philosophy

Advances in structural modeling, combined with the growth in computational processing power, have motivated and allowed the use of more accurate but, at the same time, more computationally intensive finite element models (FEMs) for seismic performance assessment. Though, these models can accurately describe inelastic/hysteretic structural behavior even for strong levels of earthquake shaking, their use for seismic risk assessment and performance-based design imposes, still, a substantial challenge, since uncertainty propagation in this setting, typically performed using Monte Carlo (MC) approaches, entails a large number of nonlinear response history analyses (NLRHAs). To alleviate this challenge, this thesis develops methodologies that can leverage lower fidelity models to accommodate higher computational efficiency while still maintaining the accuracy of an original high-fidelity FEM for a structure. The foundation of all developments considered is a new reduced order model (ROM) framework for hysteretic moment-resisting frame structures. The ROM is calibrated by explicitly comparing its nonlinear time-history response to the nonlinear FEM response, under different earthquake excitations. Two different calibration approaches are proposed: i) a deterministic, nonlinear least-squares optimization and ii) a Bayesian model updating implementation. Within the Bayesian formulation, a model class selection, model class averaging as well as a hierarchical updating are examined. An extension of the ROM framework to describe the behavior of structures with seismic protective devices (SPDs) is then examined. It is shown that the ROM calibration does not need to be updated to accurately assess the effect of the SPD addition. While the ROM can offer a substantial speedup, the corresponding predictions might be biased when compared against the ones established using the higher-fidelity FEM. A multi-fidelity Monte Carlo (MFMC) framework for seismic risk assessment is also propos

关键词： Finite element models computational processing Reduced order model

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Non-invasive method to analyse the risk of developing diabetic foot

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HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 109-113页

作者： Silva, Rebeca N. Ferreira, Ana C. B. H. Ferreira, Danton D. Barbosa, Bruno H. G. Univ Fed Lavras Dept Engn BR-37200000 Lavras MG Brazil Univ Fed Juiz de Fora Sch Nursing BR-36036900 Juiz De Fora MG Brazil

Foot complications (diabetic foot) are among the most serious and costly complications of diabetes mellitus. Amputation of all or part of a lower extremity is usually preceded by a foot ulcer. To prevent diabetic foot, an automatic non-invasive method to identify patients with diabetes who have a high risk of developing diabetic foot is proposed. To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the K-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. Both real and simulated data were used to evaluate the method in which promising results were achieved with accuracy of 0.97 + 0.06 for simulated data and 0.68 + 0.16 considering the classification of specialists as the gold standard for real data. The method requires a simple computational processing and can be useful for basic health units to triage diabetic patients helping the health-care team to reduce the number of cases of diabetic foot.

关键词： Euclidian distance K-means clustering algorithm automatic noninvasive method computational processing data classification diabetes mellitus diabetic foot diabetic patients diseases foot complications foot ulcer health care information vector medical diagnostic computing operational stage patient diagnosis pattern classification pattern clustering risk analysis self-care simulated data social scope

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