In the present study, dominant eye-gaze patterns in professional train drivers' visual behavior are investigated using the Markov Cluster (MCL) algorithm. Applying the MCL algorithm results in a common gaze patter...
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ISBN:
(纸本)9781509026784
In the present study, dominant eye-gaze patterns in professional train drivers' visual behavior are investigated using the Markov Cluster (MCL) algorithm. Applying the MCL algorithm results in a common gaze pattern showing a sort of perception tactic the drivers usually follow. The drivers repetitively move their gaze ahead soon after looking at somewhere else, independently of their years of experience. They are, however, found different in that experienced drivers can consistently follow the tactic while inexperienced drivers cannot. Time variation in the number of attentive pattern deviations demonstrates that, as well as the inexperienced drivers made higher frequency and larger fluctuations of pattern deviation, there were several particular segments in the route in which intensive pattern deviations arose in common. Inexperienced drivers would make intensive pattern deviations in such route segments that may have higher requirements of their cognitive resources.
We consider the problem of determining an unknown source, which depends only on the spatial variable, in a diffusion equation. This is an ill-posed problem. For a reconstruction of the solution from indirect data., th...
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ISBN:
(纸本)9781424437276
We consider the problem of determining an unknown source, which depends only on the spatial variable, in a diffusion equation. This is an ill-posed problem. For a reconstruction of the solution from indirect data., the dual least squares method generated by the family of Shannon wavelet subspaces is applied. Moreover.. a certain simple nonlinear modification of the method based on local refinements of the wavelet expansion of the noisy data is investigated.
Machine learning systems using patternrecognition largely assume that the patterns under consideration for classification are good enough to start. However, in general, the patterns are not enough or unknown for hard...
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ISBN:
(纸本)142440133X
Machine learning systems using patternrecognition largely assume that the patterns under consideration for classification are good enough to start. However, in general, the patterns are not enough or unknown for hard problem domain, Automatically generating patterns become necessary in the process of learning in many situations. A functional approach to patternrecognition is introduced. A Monte Carlo functional patternrecognition inspired by Ulam's Monte Carlo principle is also introduced. Any training process in a learning system can apply the existed patterns together with those being derived patterns from functional patternrecognition techniques proposed in this paper. The functional patternrecognition (FPR) is based on the functions defined in well-founded Zermenlo-Fraenko set theory. FPR is not only good for automatically generating patterns but also good for largely reducing the complexity of a problem.
identify criminal or pedophile in online child pornography images and video is a challenging task when the faces and other distinguish features are not shown. To address these kind of problems, the system of recogniti...
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ISBN:
(纸本)9781509016488
identify criminal or pedophile in online child pornography images and video is a challenging task when the faces and other distinguish features are not shown. To address these kind of problems, the system of recognition using androgenic hair pattern is being developed. The system of recognition presented in this paper used three main parts of methods, pre-processing methods, feature extraction with Haar Wavelet Transform level 1 decomposition and classification using Nearest Neighbor. Using 400 images of lower right legs with controlled condition, the system was analyzed. The Haar Wavelet Transformation for level 1 decomposition gave 83.48 % of average recognition precision when using 10-fold cross validation with nearest neighbor classification.
In this paper, a novel image fusion based interpolation method is proposed. soft-decision-adaptive interpolation (SAI) is one of the state of the art image interpolation algorithms. However, SAI may produce serious ar...
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ISBN:
(纸本)9784990644109;9781467322164
In this paper, a novel image fusion based interpolation method is proposed. soft-decision-adaptive interpolation (SAI) is one of the state of the art image interpolation algorithms. However, SAI may produce serious artifacts in small-scale edge areas. Bicubic interpolation performs better in preserving the fidelity of small-scale edges. But, bicubic interpolation may cause zigzag and blurring artifacts around strong edges. The proposed method combines the advantages of SAI and Bicubic together through image fusion. The artifacts in the SAI interpolated image are first detected and then removed by fusing the SAI interpolated image with the bicubic interpolated image. Experiments demonstrate the effectiveness of the proposed method.
Neuron MOS transistor (neuMOS or vMOS) mimicking the fundamental behavior of neurons at a primitive device level allows the implementation of intelligent functions directly on the integrated circuit hardware. Based on...
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Neuron MOS transistor (neuMOS or vMOS) mimicking the fundamental behavior of neurons at a primitive device level allows the implementation of intelligent functions directly on the integrated circuit hardware. Based on the vMOS technology a real-time event recognition system has been developed. A neuron MOS association processor searches for the event in the past memory having the maximum similarity to the current event presented to the system. This is based on Manhattan-distance calculation and the minimum distance search by a winner-take-all (WTA) circuitry. The computation is carried out directly on the hardware in a fully parallel architecture. A unique floating-gate analog EEPROM technology has been developed to build a vast memory system storing past events in multivalued vectors. Test circuits of key subsystems were fabricated by a double-polysilicon CMOS process and their operation was verified by measurements as well as by simulation. The vMOS circuit operation is characterized by analog computation directly conducted on an electrically floating node which is immediately followed by the thresholding action of a transistor to yield a binary decision. vMOS circuits would provide an opportunity for a very flexible softcomputing scheme, while preserving the rigorous nature of digital processing. (C) 1998 Elsevier Science Ltd. All rights reserved.
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to ge...
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ISBN:
(纸本)9781467369619
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling these large databases often too big to be processed. Data reduction techniques are therefore a very important step to prepare the data before data mining and knowledge discovery. In this paper we present a comparative study on original and reduced data to see the role data reduction in a learning task. For this purpose, we used a medical dataset;especially a vertebral column pathologies database.
This article presents the use of Customized Multi-Layer ANN based patternrecognition Technique for the numerical differential protection of a power transformer. An efficient Resilient Back Propagation trained neural ...
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The representations of outer world in the brain are considered to be undertaken by spatiotemporal activity patterns of neuronal circuits. In this study, we analyzed the transition of the internal states of the circuit...
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ISBN:
(纸本)9781509049172
The representations of outer world in the brain are considered to be undertaken by spatiotemporal activity patterns of neuronal circuits. In this study, we analyzed the transition of the internal states of the circuit of rat hippocampal neurons cultured on a multi-electrodes-array-dish. We analyzed transition of center of gravity and 64-dimensional feature-vectors of electrical activity patterns. Electrical activity pattern at a certain 5-ms-width-time-window was represented as a 64 dimensional "0-1" feature vector, and analyzed the stability. We confirmed that the reproducibility of the neuronal network activity increased during culture days. In addition, we applied similarity analysis to 64-dimensional feature vectors of neuronal activity. Using X-means algorithm, feature vectors were classified into "pattern repertories" based on the spatial distribution of activity.
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