Vibration signals are often used in the fault diagnosis of rotating machinery. However, due to the influence of complex environment, environmental noise is often doped, and the diagnostic accuracy is reduced. the trad...
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ISBN:
(纸本)9783030990756;9783030990749
Vibration signals are often used in the fault diagnosis of rotating machinery. However, due to the influence of complex environment, environmental noise is often doped, and the diagnostic accuracy is reduced. the traditional deep self-encoder is used in the noise reduction process of rotating machinery fault diagnosis. the pooling model is poor and easy to lead to over-fitting problems, and deep learning training needs a large number of labeled data. therefore, this paper proposes a reinforcement learning fault diagnosis method based on less label data. the random pooling is used to replace the pooling layer of the original convolutional self-encoder, and the exponential linear unit (ELU) is used to replace the original activation function to enhance the convolutional self-encoder. A large number of unlabeled samples are used for training, and then the deep reinforcement learning is used for network fine tuning. the experimental results of the sensor data collected by the fault diagnosis test bench show that the method used has a good improvement in denoising ability and feature extraction ability, and the recognition accuracy and stability are better than traditional convolutional autoencoder and traditional machinelearning methods.
Text summarization plays a crucial role in managing the overwhelming volume of information available today. this task aims to condense large amounts of information into summaries. However, the lack of large-scale anno...
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Least squares regression has shown promising performance in the supervised classification. However, conventional least squares regression commonly faces two limitations that severely restrict their effectiveness. Firs...
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ISBN:
(纸本)9789819984343;9789819984350
Least squares regression has shown promising performance in the supervised classification. However, conventional least squares regression commonly faces two limitations that severely restrict their effectiveness. Firstly, the strict zero-one label matrix utilized in least squares regression provides limited freedom for classification. Secondly, the modeling process does not fully consider the correlations among samples from the same class. To address the above issues, this paper proposes the interclass sparsity-based non-negative transition sub-space learning (ICSNTSL) method. Our approach exploits a transition sub-space to bridge the raw image space and the label space. By learning two distinct transformation matrices, we obtain a low-dimensional representation of the data while ensuring model flexibility. Additionally, an inter-class sparsity term is introduced to learn a more discriminative projection matrix. Experimental results on image databases demonstrate the superiority of ICSN-TSL over existing methods in terms of recognition rate. the proposed ICSN-TSL achieves a recognition rate of up to 98% in normal cases. Notably, it also achieves a classification accuracy of over 87% even on artificially corrupted images.
Manipulation of corporate environmental information disclosure is highly covert, posing significant challenges to the identification and judgment of such manipulative behaviors. In this study, we apply machine learnin...
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Withthe rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty...
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In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the co...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the cortical regions of ASD subjects. Every cortical feature receives a score corresponding to their contribution in diagnosing a subject to be ASD or typically developed (TD). Scores are determined by hyper-optimized machinelearning (ML) classifiers. An early personalized diagnosis of ASD becomes possible by the proposed CAD system. the proposed framework implements multiple stages including cerebral cortex extraction from structure MRI (sMRI). Moreover, the proposed framework identify the altered brain regions. We can summarize this framework in the following procedures: i) Cerebral cortex segmentation, ii) Parcellation of the cortex to Desikan-Killiany (DK) atlas;iii) Annotating brain regions which are associated with ASD;iv) Blocking for the confounding effect of both age and sex;v) Tailoring ASD neuroatlases;vi) Classifying ASD using neural networks (NN). We utilized Autism Brain Imaging data Exchange (ABIDE I) dataset to test the proposed framework. the proposed achieved a balanced accuracy score of 97% +/- 2%. In this study, we demonstrate the ability to describe specific developmental patterns of the brain in autism using tailored neuro-atlases, as well as, developing an objective CAD system using morphological features extracted from sMRI scans.
the incorporation of machinelearning techniques in medical research has facilitated the exploration of novel avenues for the timely identification of diseases. the continuous progress in medical technology has facili...
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ISBN:
(纸本)9783031530845
the incorporation of machinelearning techniques in medical research has facilitated the exploration of novel avenues for the timely identification of diseases. the continuous progress in medical technology has facilitated the acquisition of complex and complete datasets, which in turn enhances the ability to identify medical diseases in their early stages. Alzheimer’s disease, a significant and hard problem, is characterised by the slow degeneration of brain cells and has a profound impact on cognitive functions, namely memory. It occupies a prominent position within this domain. In the middle of these exciting promises, there remains a significant research gap that pertains to the absence of thorough empirical evidence about the effectiveness of machinelearning algorithms in the early identification of Alzheimer’s disease. the primary objective of this study is to address the existing research gap by conducting a comprehensive and meticulous series of experiments. A comprehensive examination of data obtained from sophisticated neuroimaging technologies is performed by utilising a wide range of machinelearning models, such as Logistic Regression, Naive Bayes, Neural Networks, Random Forest, and the Stack ensemble. the primary objective is to facilitate the prompt detection of Alzheimer’s disease, hence enabling expedited interventions and therapeutic approaches. As one embarks on the journey of research, the unfolding narrative is shaped by the use of empirical evidence, establishing a strong foundation in the convergence of state-of-the-art technology and the urgent healthcare need to detect early stages of Alzheimer’s disease. Furthermore, this research not only addresses existing gaps in the literature but also ends in the identification of the most effective machinelearning model, specifically the Neural Network, which has an accuracy rate of 87%. this significant advancement represents a critical juncture in the diagnosis of Alzheimer’s disease and sets a h
Betel nut pests and diseases are one of the important factors affecting the yield of betel nut. As a major province of betel nut production in Hainan, China, betel nut pests and diseases have seriously lost the local ...
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Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue ...
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Out-of-distribution detection, identifying unexpected data from the known concepts, is essential for reliable machinelearning. We present a novel method that explores the application of a text-to-image diffusion mode...
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ISBN:
(纸本)9789819985517;9789819985524
Out-of-distribution detection, identifying unexpected data from the known concepts, is essential for reliable machinelearning. We present a novel method that explores the application of a text-to-image diffusion model for out-of-distribution detection. Our method is motivated by the fact that the text-to-image diffusion model has shown remarkable capability in generating high-quality images with diverse text descriptions. the text description generates a corresponding text embedding and is injected into the diffusion model to affect image generation. this demonstrates that its internal representation contains semantic information and is highly enhanced by text concepts. this inspires us to apply the diffusion model to extract image representations with suitable text embeddings. In addition, we noticed that describing images directly using native text is often vague and lacking in detail. thus, we propose an implicit captioner to generate text embeddings for the input images. Subsequently, a compression head is introduced to compress the representations, facilitating easy comparison and removal of noise information. We formulate the proposed text-to-image diffusion model, implicit captioner, and compression head into a network, which we call ODDM: Out-of-distribution Detection with Text-to-Image Diffusion Models. Several experiments shows that our method can achieved superior performance.
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