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检索条件"主题词=Autoencoder"

共 4241 条记录，以下是4171-4180 订阅

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EXTENDED PIPELINE FOR CONTENT-BASED FEATURE ENGINEERING IN MUSIC GENRE RECOGNITION

EXTENDED PIPELINE FOR CONTENT-BASED FEATURE ENGINEERING IN M...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Tina Raissi Alessandro Tibo Paolo Bientinesi RWTH Aachen University AICES Schinkelstr. 2 52062 Aachen Germany University of Florence Department of Information Engineering Via S. Marta 3 50139 Firenze Italy

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.

关键词： Musical signal genre classification feature extraction and selection information gain autoencoder information gain pipeline Engineering Multiple signal classification

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Intrusion Detection Algorithm Based on Deep Learning for Industrial Control Networks

Intrusion Detection Algorithm Based on Deep Learning for Ind...

引用

作者： Yongzhong Li Yi Li Shipeng Zhang School of computer science Jiangsu University of Science and Technology

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.

关键词： Industrial Control Network Security Intrusion Detection Deep Learning autoencoder Extreme Learning Machine

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Deep Learning Tracker via SVM Ranking Vector

Deep Learning Tracker via SVM Ranking Vector

引用

2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Bo Dai Zhiqiang Hou Wangsheng Yu Xin Wang Dan Hu Institute of Information and Navigation Air Force Engineering University

The deep learning based trackers can always achieve high tracking precision and strong adaptability in different scenarios. However, due to the fact that the number of the parameter is large and the fine-tuning is challenging, the time complexity is high. In order to improve the efficiency, we proposed a tracker based on fast deep learning through constructing a new network with less redundancy. Based on the theory of deep learning, we proposed a deep neural network to describe essential features of images. Furthermore, fast deep learning can be achieved by restricting the size of network. With the help of GPU, the time complexity of the network training is released to a large extent. Under the framework of particle filter, the proposed method combined the deep learning extractor with an SVM scoring professor to distinguish the target from the background. The condensed network structure reduced the complexity of the model. Compared with some other deep learning based tracker, the proposed method can achieve higher efficiency. The frame rate keeps at 22 frames per second on average. Experiments on an open tracking benchmark demonstrate that both the robustness and the timeliness of the proposed tracker are promising when the appearance of the target changes containing translation, rotation and scale or the interference containing illumination, occlusion and cluttered background. Unfortunately, it is not robust enough when the target moves fast or the motion blur and some similar objects exist.

关键词： visual tracking deep learning SVM autoencoder

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An End-End Method for Handwritten Xibo Font Generation 21

An End-End Method for Handwritten Xibo Font Generation

引用

Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Chunju Wang YI Tang Zuo Jiang WenBiao Zhang School of Mathematics and Computer Science Yunnan Minzu University China

ISBN: (纸本)9781450384087

The generation of handwritten Xibo characters is a key step to explore the secrets of this original text. At the same time, it is also a scientific aid to the current task of rescuing and protecting Xibo characters. Based on the peculiarities of the structure of the Xibo characters, the prevailing customs of plagiarism from generation to generation, and the reasons for the difficulty of obtaining them at present, the generation of handwritten Xibo characters is a very challenging task. Based on the development of existing handwritten fonts in the field of machine learning, combined with the characteristics of the collected handwritten Xibo font data set, we propose to use a generative adversarial network to try to generate handwritten Xibo fonts. Try the existing generative adversarial network models, they are uncomfortable with the task of generating handwritten Xibo characters. Therefore, this paper proposes a feature adversarial generative model combined with an autoencoder. Using this model to generate handwritten Xibo fonts, the experimental results show that this model can stably generate various handwritten Xibo font images.

关键词： autoencoder Feature adversarial Generative adversarial network Handwritten Xibo font generation

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A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map

A Personal Conversation Assistant Based on Seq2seq with Word...

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International Congress on Advanced Applied Informatics

作者： Maoyuan Shen Runhe Huang Graduate School of Computer and Information Sciences Hosei University Faculty of Computer and Information Sciences Hosei University

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.

关键词： WeChat Word2vec Seq2seq Attention Mechanism autoencoder

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Network Service Identification Method for Electric Power Cloud

Network Service Identification Method for Electric Power Clo...

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第34届中国控制与决策会议

作者： Yilu Chen Tongtong Li Zhiyao Liu Zhichao Xu Zibo Wang Hongri Liu School of Computer Science and Technology Harbin Institute of Technology School of Cyber Science and Technology Harbin Institute of Technology China Industrial Control Systems Cyber Emergency Response Team

Electric power cloud data center becomes more and more widely applied,and yet lacks security protection inside *** most direct solution for this issue is internal network *** paper focuses on the first task of the isolation,namely network service identification,and proposes a network service identification framework based on deep *** order to obtain service stream,a network service stream extraction algorithm is proposed based on the flow metric and traffic *** the high dimensionality and complexity of service streams,the denoising and convolutional autoencoders are combined for feature *** then a self-organizing mean maps network is adopted to achieve network service *** results illustrate the effectiveness and superiority of the self-organizing mean maps network with regard to the internal evaluation index of ***,the identification framework proposed seems to provide a foundation for subsequent isolation study.

关键词： Network service identification Deep clustering autoencoder Self-organizing map

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Large-Scale Prediction of Drug-Target Interactions from Deep Representations

Large-Scale Prediction of Drug-Target Interactions from Deep...

引用

International Joint Conference on Neural Networks

作者： Peng-Wei Hu Keith C. C. Chan Zhu-Hong You Department of Computing Hong Kong Polytechnic University

ISBN: (纸本)9781509006212

Identifying drug-target interactions (DTIs) is a major challenge in drug development. Traditionally, similarity-based methods use drug and target similarity matrices to infer the potential drug-target interactions. But these techniques do not handle biochemical data directly. While recent feature-based methods reveal simple patterns of physicochemical properties, efficient method to study large interactive features and precisely predict interactions is still missing. Deep learning has been found to be an appropriate tool for converting high-dimensional features to low-dimensional representations. These deep representations generated from drug-protein pair can serve as training examples for the interaction predictor. In this paper, we propose a promising approach called multi-scale features deep representations inferring interactions (MFDR). We extract the large-scale chemical structure and protein sequence descriptors so as to machine learning model predict if certain human target protein can interact with a specific drug. MFDR use Auto-Encoders as building blocks of deep network for reconstruct drug and protein features to low-dimensional new representations. Then, we make use of support vector machine to infer the potential drug-target interaction from deep representations. The experiment result shows that a deep neural network with Stacked Auto-Encoders exactly output interactive representations for the DTIs prediction task. MFDR is able to predict large-scale drug-target interactions with high accuracy and achieves results better than other feature-based approaches.

关键词： autoencoder Drug-target interaction Deep representations representations Support Vector Network drug development Pharmaceutical Preparations

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An Intelligent Forecasting Method for Cigarette Equipment Maintenance Based on Fuzzy Mathematics Improved Analytic Hierarchy Process

An Intelligent Forecasting Method for Cigarette Equipment Ma...

引用

作者： Li.Ming,Zhu Min,Zhang Qiang,Zhang Information Center China Tobacco Zhejiang Industrial Co.LTD.

Predictive maintenance uses improved Fuzzy Analytic Hierarchy Process(FAHP) model to calculate the weight of equipment failure and the comprehensive index information of equipment failure, to identify its deterioration trend, and to judge its trend, to calculate the comprehensive maintenance threshold, to generate maintenance decision information and to identify the equipment locations that need to be disposed of.

关键词： Anomaly detection and localization cigarette packaging unsupervised learning autoencoder attention map

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Toward In-the-Field Canine Manifold Learning: Data Fusion for Evaluation of Potential Guide Dogs 23

Toward In-the-Field Canine Manifold Learning: Data Fusion fo...

引用

Proceedings of the Tenth International Conference on Animal-Computer Interaction

作者： Devon Martin David Roberts Alper Bozkurt Electrical Engineering North Carolina State University USA Computer Science North Carolina State University USA

ISBN: (纸本)9798400716560

We seek to better classify canine behavior for guide dog training predictions. Dog temperament is a major factor in success rates and current training also has a blind spot when the puppies are with puppy raisers, who are lesser trained volunteers who socialize puppies up to 15 months old. We have used a custom designed smart collar to collect environmental and behavioral data from each puppy individually going through various parts of the guide dog training. We investigate long short-term memory networks (LSTMs), autoencoders (AE), and kernel principal component analysis (KPCA) as methods to identify canine behavior and use multi-sensor data fusion to find the best subset of sensors with the best at classifying temperament. Standard manifold learning experiments take place in controlled environments and translate poorly to real-world applications. This research aims to bridge this gap using guide dog In For Training (IFT) data, which is from a lesser controlled environment and use it to develop a broader data-pattern-to-behavior dictionary for future real-world canine studies.

关键词： KPCA LSTM autoencoder canine monitoring manifold learning

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Intrusion Detection Algorithm Based on Deep Learning for Industrial Control Networks 2019

Intrusion Detection Algorithm Based on Deep Learning for Ind...

引用

Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering

作者： Yongzhong Li Yi Li Shipeng Zhang School of computer science Jiangsu University of Science and Technology Zhenjiang Jiangsu China

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.

关键词： Industrial Control Network Security Intrusion Detection autoencoder Deep Learning Extreme Learning Machine

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