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检索条件"主题词=inference algorithms"

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In-Sensor Movement Variability Tracking

In-Sensor Movement Variability Tracking

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Swapnil Sayan Saha Krishna Chaitanya Palle Mahesh Chowdhary STMicroelectronics Inc. Santa Clara CA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Movement variability tracking traces the spatiotemporal movement of limbs against a pre-recorded action template. This paper introduces an ultra-low-power, low footprint, and data-efficient movement variability detection algorithm implemented on-chip on an inertial sensor die. During the training phase, the user attaches an inertial sensor to the target limb and performs limb movements under supervision. The algorithm within the sensor automatically segments the region of interest with maximal entropic density and generates approximate gravity vector templates of the action primitive. During the inference phase, weighted similarity metrics provide the latent geometric distance between temporal portions of the stored templates and live template as heatmaps. These heatmaps provide quantitative and actionable information on which parts of the movement trajectory to correct. The algorithm requires under 6 seconds of training data, under 7 kB of memory and 0.2 mA of current on-sensor, and has a temporal resolution of 0.5 seconds. The concept benefits a broad application spectrum, including sports analysis, exercise monitoring, rehabilitation, and gait tracking.

关键词： Heating systems Weight measurement Inertial sensors Measurement uncertainty Optical variables measurement Feature extraction Approximation algorithms inference algorithms Classification algorithms Trajectory

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Implementation and Analysis of Sparse DNN on GPU

Implementation and Analysis of Sparse DNN on GPU

引用

IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Aparna Nair M K Nandhu Sam Minu A Pillai Nalesh S Kala S Department of ECE Indian Institute of Information Technology Kottayam Kerala India Institute for Integrated Programmes and Research in Basic Sciences Mahatma Gandhi University Kerala India Department of Electronics Cochin University of Science and Technology Kerala India

ISBN: (数字)9798331533229

ISBN: (纸本)9798331533236

Deep Neural Networks are being widely used in applications like object detection, image classification, etc. Having billions of parameters in the networks poses challenges in terms of storage requirements, and computational complexity. Exploiting sparsity in weights and input activations results in reduced memory and accelerated computations. This paper evaluates the performance of a sparse neural network inference on GPU that utilizes a clustering technique to convert the intermediate results into sparser representation during inference time with a sparse matrix storage format. The methodology utilizes Delta-Compressed Sparse Row sparse weight storage format for reduced memory requirements. The proposed approach has been implemented on NVIDIA Tesla T4 GPU, using medium-scale sparse DNN trained with MNIST dataset. Our implementation has achieved an average reduction of 4% in storage requirement and a maximum speedup of 1.92 × compared to baseline SNICIT implementation.

关键词： Embedded systems Single instruction multiple data Memory management Graphics processing units Artificial neural networks Object detection inference algorithms Sparse matrices Computational complexity Image classification

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Knowledge Acquisition and Insider Threat Prediction in Relational Database Systems

Knowledge Acquisition and Insider Threat Prediction in Relat...

引用

IEEE International Conference on Computational Science and Engineering, CSE

作者： Qussai Yaseen Brajendra Panda Department of Computer Science and Computer Engineering University of Arkansas Fayetteville AR USA

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferred about them using dependency relationships. Moreover, it introduces an algorithm to determine the knowledgebase of an insider and explains how insiders can broaden their knowledge about various relational database objects to which they lack appropriate access privileges. In addition, it demonstrates how NDIGs and knowledge graphs help in assessment of insider threats and what security officers can do to avoid such threats.

关键词： Knowledge acquisition Relational databases Protection Data security Data engineering Knowledge engineering inference algorithms Information security Computer science Communication system security

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Approximate discrete probability distribution representation using a multi-resolution binary tree

Approximate discrete probability distribution representation...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： D. Bellot Gravir/IMAG CNRS and INRIA Rhône-Alpes Saint Ismier France

Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on a binary-tree representation is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability distribution and to an anytime representation of the distribution.

关键词： Probability distribution Binary trees Random variables Bayesian methods Character generation inference algorithms Distributed computing Computational efficiency Graphical models Calculus

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Tracking of coordinated groups using marginalised MCMC-based Particle algorithm

Tracking of coordinated groups using marginalised MCMC-based...

引用

IEEE Conference on Aerospace

作者： Francois Septier Sze Kim Pang Simon Godsill Avishy Carmi Signal Processing Laboratory Engineering Department University of Cambridge UK

In this paper, we address the problem of detection and tracking of group and individual targets. In particular, we focus on a group model with a virtual leader which models the bulk or group parameter. To perform the sequential inference, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm with a marginalisation scheme using pairwise Kalman filters. Numerical simulations illustrate the ability of the algorithm to detect and track targets within groups, as well as infer both the correct group structure and the number of targets over time.

关键词： Particle tracking Target tracking Signal processing algorithms inference algorithms Bayesian methods Monte Carlo methods Laboratories Numerical simulation Filters Filtering algorithms

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FA*: a stimulation-based search approach

FA*: a stimulation-based search approach

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IEEE Southeastcon

作者： J. Dong Department of Computer Science Old Dominion University Norfolk VA USA

A fuzzy heuristic search approach called FA* is developed. As opposed to the point of view of probability, a more rational heuristic search approach is proposed to describe the mode of thinking with a combination of fuzzy set theory. The major characteristic of this approach is that it takes into account the considerable influence of subjective factors in the construction of the model.< >

关键词： Probability Statistics Artificial intelligence Humans inference algorithms Bayesian methods Computer science Natural languages Graphics Statistical analysis

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Application of a self-organizing fuzzy control to the joint control of a Puma-760 robot

Application of a self-organizing fuzzy control to the joint ...

引用

EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications

作者： K.C. Koh H.S. Cho S.K. Kim I.S. Jeong Department of Production Engineering Korea Advanced Institute of Science and Technology Seoul South Korea Robotics R & D Team Samsung Electronics Company Limited Suwon South Korea

The control performance of the present self-organizing control (SOC) algorithm is often limited due to quantization of process variables and large amount of computation time required within small sampling time. To improve these limitations this paper presents a combined control structure in which the conventional P-control law is used in parallel with a modified fuzzy SOC algorithm. To run the algorithm fast, the inference mechanism of the fuzzy rules is simplified by modifying the SOC algorithm. Addition of the P-control law is made in the feedforward path to guarantee stability at initial stage before the rules are completely created. To test the performance of the proposed control scheme and structure, a series of experiments were performed using a PUMA-760 robot. The experimental results show that the proposed structure can be effectively applied to the control of robot manipulators when implemented to the personal computer.< >

关键词： Fuzzy control inference algorithms Quantization Sampling methods inference mechanisms Stability Testing Performance evaluation Robot control Manipulators

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HMM parameter reduction for practical gesture recognition

HMM parameter reduction for practical gesture recognition

引用

International Conference on Automatic Face and Gesture Recognition

作者： Stjepan Rajko Gang Qian Arts Media and Engineering Program Arizona State University Tempe AZ USA

We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Markov models. We show that the reduced parameter models are comparable to standard HMM-based gesture recognition models in their ability to effectively model gestures, and in some cases superior when training data is limited. We also show that in order to effectively differentiate similar gestures, a gesture recognition model must utilize a large number of states, a scenario which can only be adequately handled by reducer parameter methods to maintain real-time speeds.

关键词： Hidden Markov models Training data Testing inference algorithms Computational complexity Pattern recognition Art Probability Libraries Usability

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Parametric estimation of Dempster-Shafer belief functions

Parametric estimation of Dempster-Shafer belief functions

引用

International Conference on Information Fusion

作者： M. Zribi M. Benjelloun Laboratoire d Analyse des Systemes du Littoral Université du Littoral Côte d''Opale Calais France

ISBN: (纸本)0972184449

Dempster-Shafer theory of evidence offers a natural setting for representing imprecise and uncertain information stemming from several sources. The application of the evidence theory in fusing information coming from different sources still poses certain problems. Of paramount importance is the problem of estimating the belief functions. Due to the coherence of this theory with the Bayesian approach, the belief functions can be represented by probabilities (a priori and a posteriori probabilities). In this paper, we propose an algorithm to estimate these belief functions. The algorithm is iterative and based on the maximum likelihood estimators. The interest of the proposed algorithm and its potential are studied stoning from a few simple simulations.

关键词： Iterative algorithms Bayesian methods Maximum likelihood estimation Information analysis Redundancy Information resources Iterative methods Stability inference algorithms Uncertainty

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Multiple disease (fault) diagnosis with applications to the QMR-DT problem

Multiple disease (fault) diagnosis with applications to the ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Feili Yu Fang Tu Haiying Tu K. Pattipati Department of Electrical & Computer Engr University of Connecticut Storrs CT USA

In this paper, we present three classes of computationally efficient algorithms that can handle cases with hundreds of positive findings in QMR-DT(Quick Medical Reference, Decision-Theoretic) Network. These include Lagrangian Relaxation Algorithm (LRA), Primal Heuristic Algorithm (PHA), and Approximate Belief Revision Algorithm (ABR). These algorithms solve the QMR-DT problem by finding the most likely set of diseases given the findings. Extensive computational experiments have shown that LRA obtains the best solutions among the three algorithms proposed within a relatively small processing time. We also show that the Variational Probabilistic inference method is a special case of our LRA. The solutions are generic and have application to multiple fault diagnosis in complex industrial systems.

关键词： Diseases Fault diagnosis inference algorithms Medical diagnostic imaging Heuristic algorithms Bayesian methods Application software Belief propagation Stochastic processes Lagrangian functions

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