This paper describes application of Artificial Intelligence using machine learning and deep learning at our laser diode module manufacturing facility. Implementing A.I. into data analysis and classification problems, ...
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ISBN:
(纸本)9781728133379
This paper describes application of Artificial Intelligence using machine learning and deep learning at our laser diode module manufacturing facility. Implementing A.I. into data analysis and classification problems, various benefits such as quality control, human work reduction and efficient usage of big data have been obtained.
Recently, a nonlinear dimension reduction technique, called autoencoder, had been *** can efficiently carry out mappings in both directions between the original data and low-dimensional code ***, a single autoencoder ...
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Recently, a nonlinear dimension reduction technique, called autoencoder, had been *** can efficiently carry out mappings in both directions between the original data and low-dimensional code ***, a single autoencoder commonly maps all data into a single *** the original data set have remarkable different categories (for example, characters and handwritten digits), then only one autoencoder will not be efficient To deal with the data of remarkable different categories, this paper proposes an Auto-Associative Neural Network System (AANNS) based on multiple *** novel technique has the functions of auto-association, incremental learning and local ***, these functions are the foundations of cognitive *** results on benchmark MNIST digit dataset and handwritten character-digit dataset show the advantages of the proposed model.
While Word2Vec represents words (in text) as vectors carrying semantic information, audio Word2Vec was shown to be able to represent signal segments of spoken words as vectors carrying phonetic structure information. ...
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ISBN:
(纸本)9781538646595
While Word2Vec represents words (in text) as vectors carrying semantic information, audio Word2Vec was shown to be able to represent signal segments of spoken words as vectors carrying phonetic structure information. Audio Word2Vec can be trained in an unsupervised way from an unlabeled corpus, except the word boundaries are needed. In this paper, we extend audio Word2Vec from word-level to utterance-level by proposing a new segmental audio Word2Vec, in which unsupervised spoken word boundary segmentation and audio Word2Vec are jointly learned and mutually enhanced, so an utterance can be directly represented as a sequence of vectors carrying phonetic structure information. This is achieved by a segmental sequence-to-sequence autoencoder (SSAE), in which a segmentation gate trained with reinforcement learning is inserted in the encoder. Experiments on English, Czech, French and German show very good performance in both unsupervised spoken word segmentation and spoken term detection applications (significantly better than frame-based DTW).
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is...
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ISBN:
(纸本)9781509041183
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music.
A variety of techniques based on numerical characteristics are currently presented for mining time-series data. However, we find that time-series data generally contain curves sharing some set of visual characteristic...
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A variety of techniques based on numerical characteristics are currently presented for mining time-series data. However, we find that time-series data generally contain curves sharing some set of visual characteristics and *** characteristics offer a deeper understanding of time-series data, and open up a potential new technique for time-series analysis. Particularly beneficial from recent advances in deep neural networks, representations and features can be automatically learnt by deep learning architectures such as autoencoders. Based on that, our work proposes a novel method, named time-series visualization(TSV), to efficiently detect visual characteristics from curves of time-series data and use these characteristics for intelligent analysis. Architecture and algorithm of TSV based on stacked autoencoders are introduced in this paper. Further, important factors affecting the performance of TSV are discussed based on empirical results. Through empirical evaluation, it is demonstrated that TSV has better efficiency and higher classification accuracy on analyzing the datasets with significant curve feature.
At present, the smart assembly of mobile phones is a challenging task. The assembly process will be affected by console vibration, illumination changes, and end self-occlusion, and it is difficult to locate directly t...
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ISBN:
(纸本)9781450398343
At present, the smart assembly of mobile phones is a challenging task. The assembly process will be affected by console vibration, illumination changes, and end self-occlusion, and it is difficult to locate directly through visual methods. In order to solve the above problems, this paper proposes a multi-modal skill learning model for improving the accuracy of mobile phone assembly. By fusing visual and tactile information, the deep learning model is used to guide the intelligent assembly of robots to improve the accuracy and success rate of mobile phone assembly. The method in this paper has carried out the experiment of flexible flat cable assembly on Redmi note 11, and can ensure the assembly error of the flexible flat cable X and Y axes within 0.3mm. This method is expected to be applied in a real assembly environment, improve the success rate of intelligent assembly and reduce the cost of mobile phone assembly.
As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed ...
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ISBN:
(纸本)9781728118680
As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.
In this paper, we present a novel deep learning model termed Deep Autoencoding-Classification Network (DACN) for HEp-2 cell classification. The DACN consists of an au-toencoder and a normal classification convolutiona...
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ISBN:
(纸本)9781509011735
In this paper, we present a novel deep learning model termed Deep Autoencoding-Classification Network (DACN) for HEp-2 cell classification. The DACN consists of an au-toencoder and a normal classification convolutional neural network (CNN), while the two architectures shares the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error based on a multi-task learning procedure. We evaluate the proposed model using the publicly available ICPR2012 benchmark dataset. We show that this architecture is particularly effective when the training dataset is small which is often the case in medical imaging applications. We present experimental results to show that the proposed approach outperforms all known state of the art HEp-2 cell classification methods.
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