In this paper,we describe a intrusion detection algorithm based on deep learning for industrial control networks,aiming at the security problem of industrial control *** learning is a kind of intelligent algorithm and...
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In this paper,we describe a intrusion detection algorithm based on deep learning for industrial control networks,aiming at the security problem of industrial control *** learning is a kind of intelligent algorithm and has the ability of automatically *** use self-learning to enhance the experience and dynamic classification *** ideology of deep learning is similar to the idea of intrusion detection to improve the detection rate and reduce the rate of false through learning,a sparse auto-encoder-extreme learning machine intrusion detection model is proposed for the problem of intrusion detection *** uses deep learning autoencoder to combine the coefficient penalty and reconstruction loss of the encode layer to extract the features of high-dimensional data during the training model,and then uses the extreme learning machine to quickly and effectively classify the extracted *** accuracy of the algorithm is verified by the industrial control intrusion detection standard data *** experimental results verify that the method can effectively improve the performance of the intrusion detection system and reduce the false alarm rate.
Thanks to its hierarchical and generative nature,Deep Belief Network（DBN） is effective to feature representation and extraction in signal *** this paper,DBN is investigated and implemented to monaural speech ***,two...
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Thanks to its hierarchical and generative nature,Deep Belief Network（DBN） is effective to feature representation and extraction in signal *** this paper,DBN is investigated and implemented to monaural speech ***,two separate DBNs are trained to extract features from mixed noisy signals and target clean speech ***,the two types of extracted features are associated together by training a BP neural network to obtain a mapping from the features of mixed signals to the features of target ***,by performing DBN and the above mapping neural network,target speech can be estimated from the input mixed *** are conducted on different kinds of mixed signals including female/male speech mixtures,human-speech/Gaussian-noise audio mixtures,and human-speech/music audio *** PESQ scores of the extracted speech are 3.32,2.59,and 3.42 respectively,which illustrates that the model performs well on speech separation tasks,especially on the mixed signals where the inference signals have obvious spectral structures.
While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel framew...
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While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery. The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand–protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learni
WeChat is one of social network applications that connects people widely. Huge data is generated when users conduct conversations, which can be used to enhance their lives. This paper will describe how this data is co...
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ISBN:
(纸本)9781538674482;9781538674475
WeChat is one of social network applications that connects people widely. Huge data is generated when users conduct conversations, which can be used to enhance their lives. This paper will describe how this data is collected, how to develop a personalized chatbot using personal conversation records. Our system will have a cognitive map based on the word2vec model, which is used to learn and store the relationship of each word that appears in the chatting records. Each word will be mapped to a continuous high dimensional vector space. Then we will adopt the sequence-to-sequence framework (seq2seq) to learn the chatting styles from all pairs of chatting sentences. Meanwhile, we will replace the traditional one-hot embedding layer with our word2vec embedding layer in the seq2seq model. Furthermore, we trained an autoencoder of seq2seq architecture to learn the vector representation of each sentence, then we can evaluate the cosine similarity between model generated response and the pre-existing response in test set, and we can also display the distance with principal component analysis (PCA) projection. As a result, our word2vec embedded seq2seq model significantly outperforms the one-hot embedded one.
Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence to-sequence autoencoder (SA). These vector representations are shown to describe the sequential ph...
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ISBN:
(纸本)9781538646595
Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence to-sequence autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as spoken term detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch the phonetic structure from the audio segments of the target language if the source and target languages are similar. In STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.
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.
In this paper, we describe a intrusion detection algorithm based on deep learning for industrial control networks, aiming at the security problem of industrial control network. Deep learning is a kind of intelligent a...
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ISBN:
(纸本)9781450376228
In this paper, we describe a intrusion detection algorithm based on deep learning for industrial control networks, aiming at the security problem of industrial control network. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It use self-learning to enhance the experience and dynamic classification capabilities. The ideology of deep learning is similar to the idea of intrusion detection to improve the detection rate and reduce the rate of false through learning, a sparse auto-encoder-extreme learning machine intrusion detection model is proposed for the problem of intrusion detection accuracy. It uses deep learning autoencoder to combine the coefficient penalty and reconstruction loss of the encode layer to extract the features of high-dimensional data during the training model, and then uses the extreme learning machine to quickly and effectively classify the extracted features. The accuracy of the algorithm is verified by the industrial control intrusion detection standard data set. The experimental results verify that the method can effectively improve the performance of the intrusion detection system and reduce the false alarm rate.
In order to avoid the occurrence of large-scale accidents to the greatest extent,it is necessary to find out the faults in the continuous operation of mechanical equipment and make corresponding *** the development of...
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In order to avoid the occurrence of large-scale accidents to the greatest extent,it is necessary to find out the faults in the continuous operation of mechanical equipment and make corresponding *** the development of artificial intelligence technology,autoencoder（AE） has been widely used in fault diagnosis,among which class level autoencoder（CLAE）effectively overcome the intra-class variations of detection data under different ***,the CLAE model just uses a single scale method to extract features from the data,and when the data is mixed with noise,the classification performance will *** this work,we proposed a multiscale class level autoencoder model（MSCLAE） which aims at learning robust and discriminative ***,we extract single-scale features from each CLAE model with different input and ouput dimensions respectively and combine these features for fault pattern *** experimental results on a motor bearing dataset have demonstrated that the proposed method can extract more robust features and obtain better anti-noise ability.
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditi...
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ISBN:
(纸本)9781538646595
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task.
Due to the complexity of modern industrial processes, there may be both linear and nonlinear relationships exist among process variables. In addition, the dynamic behavior of the process also brings challenges to proc...
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Due to the complexity of modern industrial processes, there may be both linear and nonlinear relationships exist among process variables. In addition, the dynamic behavior of the process also brings challenges to process ***, some linear monitoring methods have been developed for dynamic processes. However, the existing methods can not precisely extract the dynamic characteristics of nonlinear processes. What is more, purely linear or nonlinear methods can hardly tackle the hybrid linear and nonlinear relationships among process variables. To address the above issue, a novel method, termed slow feature networks(SFNet) is proposed and applied for dynamic process monitoring. On the one hand, a slowly varying constraint of hidden features is added to the autoencoder, so that the static and dynamic characteristics of nonlinear processes can be extracted concurrently. On the other hand, a linear mapping is incorporated into the nonlinear neural network structure,thereby providing parallel analysis of linear and nonlinear monitoring information. Five statistics are constructed for comprehensive process monitoring from both static and dynamic, linear and nonlinear perspectives. In this way, alarms corresponding to different statistical information are used to indicate different operating statuses with meaningful interpretation and enhanced process understanding. A real industrial example is adopted to validate the performance of the proposed method.
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