this paper targets the problem of finding an efficient distribution of a computational task on a heterogeneous computing platform. the heterogeneity of the processing elements arise due to differences in computation s...
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ISBN:
(纸本)9781665449137
this paper targets the problem of finding an efficient distribution of a computational task on a heterogeneous computing platform. the heterogeneity of the processing elements arise due to differences in computation speed and memory capacity of the processors. We first consider using a discrete functional performance model that integrates processing speed and capacity of processing elements and then develop a mathematical model and propose a heuristic mapping algorithm for distributing a given total workload of size N on p processing elements such that the total computation time is minimized Computational results show that the proposed method provides a significant improvement in reducing the computation time in comparison to equal distribution approach.
Withthe development of urbanization, the social demand for energy is increasing. the safety production monitoring of electric power has always been an important issue related to the national economy and people's ...
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ISBN:
(纸本)9783030962821;9783030962814
Withthe development of urbanization, the social demand for energy is increasing. the safety production monitoring of electric power has always been an important issue related to the national economy and people's livelihood. thanks to deep learning technology, a large number of monitoring and computer vision analysis algorithms have begun to popularize, but only in some simple scenes, or only after investigation and evidence collection. Due to the lack of training samples, the traditional machine learning method cannot be used to train the generative model in hazard situations. Besides, it is a pressure and challenge to the calculation capacity, bandwidth and storage of the system. this paper proposes a platform level solution based on data flow, which can use a large number of cost-effective general-purpose devices to form a cluster, and adjust the task load of each computing unit through software level resource scheduling. the equipment adopts 2U general specification, which can provide better heat dissipation and improve the cooling efficiency of the cluster. At present, the system has been deployed in several pilot projects of the State Grid. It uses LSTM algorithm to establish the contour with normal data training, and uses the deviation of 12.5% as the threshold to identify the abnormal scene. It can accurately identify the obvious suspected abnormal behavior with 98.6% and push it to the operation and maintenance personnel for secondary confirmation.
Withthe gradual rise of telemedicine, a large number of images will be transmitted on the network during network consultation, which may cause privacy disclosure. How to more effectively ensure the information securi...
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Hu's Moment corresponding to the Local Binary pattern (LBP) features vector have been frequently used for binary objects patternrecognition and classification. Hu's Moments are used in a variety of applicatio...
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Vehicular Adhoc Network (VANET) is a technology where various vehicles on the road use Wireless Adhoc network technique for making communication among them for better driving environment and laser probability of roads...
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this paper proposes a facial expression recognition system for smart learning on classroom. Firstly, YOLO is used to extract face images of multiple students from high-resolution video;secondly, face images are prepro...
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the machine learning model influences the patternrecognition based on extraction of relative patterns, but not enough capable of efficiently processing the data set that needs layered interaction. So, deep learning m...
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ISBN:
(纸本)9783030883782;9783030883775
the machine learning model influences the patternrecognition based on extraction of relative patterns, but not enough capable of efficiently processing the data set that needs layered interaction. So, deep learning model takes the advantage of artificial neural network for processing the data in layered abstraction by exploring massive parallelism, although the classical implementations of such model may not be competent due to the processing and storage of large neural networks. Current research explores quantum computation potentials and utilizes its significance for supporting inherent parallelism, as the machine perform exponential operations in single step of execution. the possible classical designs of patternrecognition using deep learning are Hopfield network and Boltzmann machine and their equivalent quantum models can remain effective and overcome the processing limitations of classical model by incorporating Grover's search method and quantum Hebbian learning. the aim of writing this article is to introduce the necessity of deep learning model, an emergence of quantum computations, discussion over requisites of data preprocessing techniques, then to propose the classical equivalent quantum deep learning model along withthe algorithms, complexity comparison and speedup analysis followed by conclusive aspects that proves effectiveness of quantum deep learning model and future works.
Research on emotion recognition from cues expressed in facial expression has a long-standing tradition. In this study, we investigate human's visual attention and fixation patterns when identifying six basic emoti...
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ISBN:
(纸本)9781665400190
Research on emotion recognition from cues expressed in facial expression has a long-standing tradition. In this study, we investigate human's visual attention and fixation patterns when identifying six basic emotions on expressive talking faces. Stimuli for the current experiments consisted of 92 video clips of facial expression during talking. the whole experiments were divided into two sessions. the video stimuli in the first session were presented in random order across different face identities, while in the second session the video from the same face identity were be played sequentially. the participants' eye movements were recorded by the Tobii X3-120 screen-based eye-tracking system. We defined a set of area-of-interest (AOI) regions, including 4 AOIs of general face areas and 12 AOIs related to specific Action Units (AUs) involved in the coding of the six basic emotions. the gaze pattern analysis was done by looking at the fixation time on this predetermined set of AOIs. Based on the ANOVA analysis, we did not find significant differences in mean fixation time on any AOI for discriminating the six basic emotions, but a subset of significant AOIs was found when we sectioned the six basic emotions into positive, negative, and neutral. Next, we propose to develop a novel emotion perception classifier which can automatically classify an observer's emotion perception based on her gaze patterns and fixation sequence when identifying the basic emotions on expressive talking faces. the fixation time on the 16 predetermined AOIs were used as features to train support vector machine (SVM) models. the proposed models achieved the overall classification accuracy of 84.1% on recognizing 3-way emotions of negative, positive and neutral, suggesting that the proposed eye gaze patterns - the fixation time on 16 predetermined AOIs, are very promising for automatic classification of the perceived emotions. Finally, we divided the data into different gender and race groups, and
In the recent era, a large number of private and corporate users redistribute the data to the cloud providers. In this extremely large amount of data, there exists large amount of copies of repeated duplicate data. th...
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