The proceedings contain 99 papers. The topics discussed include: on the multivariable iterative learning control base on the gradient method;harmonic elimination control of an inverter based on an artificial neural ne...
ISBN:
(纸本)9783902661661
The proceedings contain 99 papers. The topics discussed include: on the multivariable iterative learning control base on the gradient method;harmonic elimination control of an inverter based on an artificial neural network strategy;adaline-based approaches for time-varying frequency estimation in power systems;experimental modeling of propulsion transients of a brushless DC motor and propeller pair under limited power conditions: a neural network based approach;motion detection and tracking of classified objects with intelligent systems;real-time global optimization using multiple units;state estimation based optimal control and NARMA-L2 controllers of a scaled-model helicopter;constrained suboptimal dual control algorithms for discrete-time stochastic systems;the continuous system equivalent to the system with sliding mode control;and the air-jet texturing and twisting machine's and model predictive control based on state-space.
At present, many virtual learning communities are just simple two-dimensional replicas of former teaching materials, most virtual learning environments are not capable of realizing multi-person cooperation, and lots o...
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Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcom...
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ISBN:
(纸本)9781728151021
Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcome this limitation, we propose the Generative Object Detection which learns with only cropped images that are not in pairs. Our model based on Generative Adversarial Networks (GAN) creates cropped images by making a mask that represents the object region. To achieve this goal, we devise a novel mask mean loss (MML) that helps the GAN be able to estimate the distribution of training data and uses dilated convolution for a wider reception field in the generator. The experimental results show that Generative Object Detection improves the mIoU and accuracy.
To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) andmachinelearning algorithms such as support vector regression (SVR), extreme lea...
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ISBN:
(纸本)9781479959914
To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) andmachinelearning algorithms such as support vector regression (SVR), extreme learningmachine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.
Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not...
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ISBN:
(纸本)9781538609699
Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not met any extra ordinary success. Identification of diseased fish at early stage is necessary step to prevent from spreading disease. This paper recognizes and identifies the EUS (Epizootic Ulcerative syndrome) disease which is caused by bn Aphanomyces invadans, a fungal pathogen. It is a red spot disease and looks like ulcer hence it is generally misidentified by the people. The paper is divided into two parts, in the first part segmentation has been applied to enhance the image, various edge detection techniques have applied to get the useful information and Morphological operations have been applied on the EUS diseased fish image. In second part features are extracted from the EUS infected fish image through different feature Descriptors i.e. HOG (Histogram of Gradient), FAST (Features from Accelerated Segment Test) and classify the EUS infected and Non-EUS infected fish image through machinelearning Algorithms and find the classification accuracy through Classifier. If PCA applied after feature extraction then it increases the accuracy as it is a dimensional reduction. The proposed combination of techniques gives better accuracy as compared to the others. The Experimentation has been done on MATLAB environment on real images of EUS infected fish database.
Heart diseases are a global leading cause of death, affecting nations universally. Early detection andmachinelearning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucia...
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As the fifth generation (5G) of wireless communication rolls out worldwide, conceptualized use cases and disruptive industry solutions are being deployed to offer smooth, frictionless, and secure connectivity. The lan...
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The neural key exchange algorithm for choosing the relevant inputs is sufficient to achieve a more or less secure key-exchange protocol, however A and B could improve it by taking more information into account, includ...
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ISBN:
(纸本)9780769539607
The neural key exchange algorithm for choosing the relevant inputs is sufficient to achieve a more or less secure key-exchange protocol, however A and B could improve it by taking more information into account, including queries in the training process of the neural networks. Alternatively A and B are generating an input which is correlated with its state and A or B is asking the partner for the corresponding output bit[7]. The overlap between input and weight vector is so low that the additional information does not reveal much about the internal states. But queries introduce a mutual influence between A and B which is not available to an attacking network E. In this work query incorporated to the case of the Hebbian training rule. The probability of a successful attack is calculated for different model parameters using numerical simulations. The results show that queries restore the security against cooperating attackers.
This study introduces a novel methodology for measuring the pitch angle of Unmanned Aerial Vehicles (UAVs) by leveraging radar cross-section (RCS) data combined with machinelearning algorithms. Traditional pitch angl...
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Online advertisement is an important source of revenue for internet companies, so increasing click-through rates (CTR) on ads is crucial. the traditional CTR prediction model only extracts the classification features ...
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ISBN:
(纸本)9781450371926
Online advertisement is an important source of revenue for internet companies, so increasing click-through rates (CTR) on ads is crucial. the traditional CTR prediction model only extracts the classification features and numerical features of the advertisement, but ignores the title, description and other text features of the advertisement. However, we know that such information is very important for an advertisement. Therefore, we propose a deep multimodal network (DMN) to solve this problem. on the basis of the traditional deep model, DMN add the text features of cyclic neural network learning, so as to improve the performance of the model. we also did a lot of visual data analysis on the dataset, Finally, we conducted a comparative experiment on the real-world dataset.
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