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检索条件"主题词=autoencoder"

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AniDance : Real-Time Dance Motion Synthesize to the Song 18

AniDance : Real-Time Dance Motion Synthesize to the Song

引用

26th ACM Multimedia Conference (MM)

作者： Tang, Taoran Mao, Hanyang Jia, Jia Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450356657

In this paper, we present a demo named AniDance that can synthesize dance motions with melody in real-time. When users sing a song or play one in their phone to AniDance, their melody will drive the 3D-space character to dance to create a lively dance animation. In practice, we conduct a music oriented 3D-space dance motion dataset by capturing real dance performances, using LSTM-autoencoder to identify the relation between music and dance. Based on these technologies, users can create valid choreographies that capable of musical expression, witch can promote their learning ability and interest in dance and music.

关键词： Multisensory interaction Motion Synthesis LSTM autoencoder Music-dance dataset 3D motion capture

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A Personal Conversation Assistant Based on Seq2seq with Word2vec Cognitive Map 7

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

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7th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Shen, Maoyuan Huang, Runhe Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Hosei Univ Fac Comp & Informat Sci Tokyo Japan

ISBN: (纸本)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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Deep Learning Approach to Multiple Features Sequence Analysis in Predictive Maintenance 7th

Deep Learning Approach to Multiple Features Sequence Analysi...

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7th International Workshop of Advanced Manufacturing and Automation (IWAMA)

作者： Yuan, Jin Wang, Kesheng Wang, Yi Shandong Agr Univ Coll Mech & Elect Engn Tai An Shandong Peoples R China NTNU Dept Mech & Ind Engn Trondheim Norway Plymouth Univ Sch Business Plymouth Devon England

ISBN: (纸本)9789811057687;9789811057670

The representation learning of life cycle dataset has its particularity in the correlation of different features and the dependency of adjacent sampling time. This paper addresses the difficulty of segmentation to high-dimensional nonlinear life cycle long CBM data, and propose a new deep learning approach based on unsupervised representation learning named autoencoder for rolling bearing diagnosis. Two kinds of autoencoder with encoder and decoder model are developed respectively using fully connected and convolutional hidden layers to automatically extract the dataset's representative features. Compared to the fully connected one, the convolutional autoencoder shows clearer in a lower dimensional feature space by preserving the local neighborhood structure, and more effective to discover subjectively the intrinsic structure of nonlinear high-dimensional data of deterioration process.

关键词： Predictive maintenance Multiple features sequence Bearing life cycle Deep learning autoencoder

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Automatic Unusual Driving Event Identification for Dependable Self-Driving 18

Automatic Unusual Driving Event Identification for Dependabl...

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16th Conference on Embedded Networked Sensor Systems (SENSYS)

作者： Li, Hongyu Wang, Hairong Liu, Luyang Gruteser, Marco Rutgers State Univ WINLAB North Brunswick NJ 08902 USA

ISBN: (纸本)9781450359528

This paper introduces techniques to automatically detect driving corner cases from dashcam video and inertial sensors. Developing robust driver assistance and automated driving technologies requires an understanding of not just common highway and city traffic situations but also a plethora of corner cases that may be encountered in billions of miles of driving. Current approaches seek to collect such a catalog of corner cases by driving millions of miles with self-driving prototypes. In contrast, this paper introduces a low-cost yet scalable solution to collect such events from any dashcam-equipped vehicle to take advantage of the billions of miles that humans already drive. It detects unusual events through inertial sensing of sudden human driver reactions and rare visual events through a trained autoencoder deep neural network. We evaluate the system based on more than 120 hours real road driving data. It shows 82% accuracy improvement versus strawman solutions for sudden reaction detection and above 71% accuracy for rare visual views identification. The detection results proved useful for re-training and improving a self-steering algorithm on more complex situations. In terms of computational efficiency, the Android prototype achieves 17Hz frame rate (Nexus 5X).

关键词： Self-Driving Unusual Driving Identification autoencoder Inertial sensing

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SCORES: Shape Composition with Recursive Substructure Priors

SCORES: Shape Composition with Recursive Substructure Priors

引用

11th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SA)

作者： Zhu, Chenyang Xu, Kai Chaudhuri, Siddhartha Yi, Renjiao Zhang, Hao Simon Fraser Univ Burnaby BC Canada Natl Univ Def Technol Changsha Hunan Peoples R China Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450360081

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss. We show results of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.

关键词： shape composition recursive neural network autoencoder structural synthesis substructure prior

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DL-GSA: A Deep Learning Metaheuristic Approach to Missing Data Imputation 9th

DL-GSA: A Deep Learning Metaheuristic Approach to Missing Da...

引用

9th International Conference on Swarm Intelligence (ICSI)

作者： Garg, Ayush Naryani, Deepika Aggarwal, Garvit Aggarwal, Swati Univ Delhi Netaji Subhas Inst Technol Div Comp Engn New Delhi India

ISBN: (纸本)9783319938189;9783319938172

Incomplete data has emerged as a prominent problem in the fields of machine learning, big data and various other academic studies. Due to the surge in deep learning techniques for problem-solving, in this paper, authors have proposed a deep learning-metaheuristic approach to combat the problem of imputing missing data. The proposed approach (DL-GSA) makes use of the nature inspired metaheuristic, Gravitational search algorithm, in combination with a deep-autoencoder and performs better than existing methods in terms of both accuracy and time. Owing to these improvements, DL-GSA has wider applications in both time and accuracy sensitive areas like imputation of scientific and research datasets, data analysis, machine learning and big data.

关键词： autoencoder Missing at random Missing data imputation Gravitational search algorithm Missing completely at random

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Fast Activation Function Approach for Deep Learning Based Online Anomaly Intrusion Detection 4

Fast Activation Function Approach for Deep Learning Based On...

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4th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity) / 4th IEEE International Conference on High Performance and Smart Computing (HPSC) / 3rd IEEE International Conference on Intelligent Data and Security (IDS)

作者： Alrawashdeh, Khaled Purdy, Carla Univ Cincinnati Dept Elect Engn & Comp Syst Cincinnati OH 45221 USA

ISBN: (纸本)9781538643990

The piecewise-linear activation functions such as ReLU become the catalyst that revolutionizes the training of the deep neural networks. Common nonlinear activation functions used in neural networks such as the tanh and the sigmoid activation functions suffer from saturation during training. The saturation behavior causes the problem of vanishing stochastic gradient decent. We propose a fast activation function, namely the Adaptive Linear Function (ALF) to increase the convergence speed and accuracy of the deep leaning structure for real-time applications. The ALF reduces the saturation effects caused by the soft activation functions and the vanishing gradient caused by the negative values of the ReLU. We evaluate the training method for an online anomaly intrusion detection system using Deep Belief Network (DBN) and simulating four benchmark datasets. The activation function increases the convergence speed of the DBN, with the entire training time reduced 80% compared to the sigmoid, ReLU, and tanh activation functions. The method achieves an accuracy rate of 98.59% on the total 10% KDDCUP'99 test dataset, 96.2% on the NSL-KDD dataset, 98.4% on the Kyoto dataset, and 96.57% on the CSIC HTTP dataset. The proposed activation function outperformed the results obtained when any of the three activation functions-sigmoid, ReLu, or tanh-was used on the test stream of the four datasets. Furthermore, the DBN structure outperforms state-of-the-art networks such as the Stacked Sparse autoencoder Based Extreme Learning Machine (SSAELM) in both accuracy and convergence speed.

关键词： Deep Learning Neural Network Intrusion Detection Anomaly Detection ReLU DBN RBM autoencoder ELM Cyber Security

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Computational seismic interpretation using attention models, texture dissimilarity, and learning

Computational seismic interpretation using attention models,...

引用

作者： Shafiq, Muhammad Amir Georgia Institute of Technology

学位级别：博士

The exploration of oil and gas is a vital part of today's increasing power demands to meet the energy we need to power our homes, businesses, and transportation. Oil and gas explorers use seismic surveys, both onshore and offshore, to produce detailed images of the various rock types, layers, and their locations beneath the Earth's subsurface. The acquired data undergo a series of processing steps, which require powerful computing hardware, sophisticated software, and specialized manpower. To extract useful information from seismic data, interpreters manually delineate important geological structures, which contain hints about petroleum and gas reservoirs such as salt domes, faults, channels, fractures, and horizons. These structures typically span over several square kilometers and are delineated based on correlation, changes in illumination, intensity, contrast, and texture of seismic data. There are limited tools available for automatic detection and manual interpretation is becoming extremely time consuming and labor intensive. In this dissertation, we propose novel seismic attributes based on texture dissimilarity, visual-attention theory, the modeling of human visual system, and machine learning to quantify changes and highlight geological features in a three-dimensional space. To automate the process of seismic interpretation, we develop interpreter-assisted, fully-, and semi-automated workflows that are interactive and easy-to-use for the delineation of important geological structures within seismic volumes. Experimental results on real and synthetic datasets show that our proposed algorithms outperform the state-of-the-art methods for seismic interpretation. In a nutshell, this dissertation introduces novel seismic attributes and automated, interactive, and interpreter-assisted workflows, which have a very promising future in effective seismic interpretation. The proposed research is computationally inexpensive and is expected to not only reduce the time fo

关键词： Seismic interpretation Automation Gradient of texture Visual saliency Salsi SeisSal Unsupervised learning autoencoder 3D FFT Multispectral projections Directional center-surround Feature maps Human visual system Texture dissimilarity F3 block Netherlands North Sea SEAM Great South Basin New Zealand Stratton Listric faults Texas Gulf Coast Sparse classification

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基于特征提取和异常分类的网络流量异常检测方法

基于特征提取和异常分类的网络流量异常检测方法

引用

作者：杜臻南京邮电大学

学位级别：硕士

网络流量异常检测是网络和安全管理领域的重要研究内容。通过分析网络流量,可以评估网络环境的健康状况并对异常及攻击行为预警与防范,特别是对网络流量异常的识别和分类提供实用有效的指导。论文对基于HTTP协议的Web攻击所造成的异常... 详细信息

网络流量异常检测是网络和安全管理领域的重要研究内容。通过分析网络流量,可以评估网络环境的健康状况并对异常及攻击行为预警与防范,特别是对网络流量异常的识别和分类提供实用有效的指导。论文对基于HTTP协议的Web攻击所造成的异常流量进行识别与分类。提出新的分析角度,即分析网络流量数据包不涉及内容的外部参数,以期能够识别并区分两种Web攻击所造成的流量异常。通过一系列预处理和选择工作得到所需要的外部参数。从这个角度出发,论文提出两种分析模型:一种是基于小波分解提取能量特征的分类方法,将外部参数看做信号利用小波函数进行分解并提取能量相关特征并将特征作为分类器的训练数据;一种是基于autoencoder压缩并提取特征的分类方法,将外部参数送入搭建的autoencoder深度网络中,该网络自学习到表达特征并将特征送入分类器进行训练与预测。通过定性与定量的对比实验,两种方法均可得到较优的分类性能,分类精确度在88%以上,特别是基于小波分解提取能量特征的分类方法,分类精确度达到91%。针对两种Web攻击所造成的网络流量异常现象进行识别分类,实验结果证明论文所提出的方法是可行的,同时在网络流量的分析角度上也有一定的可参考性。

关键词： Web攻击网络流量异常特征提取小波分析 autoencoder 分类算法

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汽车测试的HIL设计与驾驶行为标准化研究

汽车测试的HIL设计与驾驶行为标准化研究

引用

作者：徐恺浙江大学

学位级别：硕士

汽车电子控制系统(Automotive electronic control system)在汽车领域的重要性日益增强,它让汽车变得更加安全、舒适和节能。现阶段汽车电子控制系统正变得越来越复杂,而汽车电子控制系统中以动力总成模块PCM最为复杂和核心,为了解决PC... 详细信息

汽车电子控制系统(Automotive electronic control system)在汽车领域的重要性日益增强,它让汽车变得更加安全、舒适和节能。现阶段汽车电子控制系统正变得越来越复杂,而汽车电子控制系统中以动力总成模块PCM最为复杂和核心,为了解决PCM控制功能复杂化与快速开发之间的矛盾,V模式开发流程开始被各大汽车公司应用到汽车电子控制系统中。HIL是V模式开发流程中非常重要的环节,通过HIL仿真测试系统就可以在实验室对PCM进行测试,极大的缩短了开发的时间周期、降低了开发的成本。对驾驶行为进行定性或定量的建模与评估是无人驾驶、智能交通等领域的重要课题和关键技术,在车辆性能测试、辅助驾驶和异常驾驶行为的诊断中都有十分重要的应用价值。从汽车制造厂商的角度来看,了解汽车驾驶员的驾驶行为偏好对优化汽车的驾驶行为体验和个性化的提升车辆的动力学性能都有十分重要的意义。本文通过建立驾驶行为的标准化模型对驾驶员的驾驶行为偏好进行学习,将不同路段、不同环境的驾驶行为映射到联邦车辆测试曲线中,实现对不同驾驶行为的标准化。本文在充分了解了汽车测试的HIL系统发展现状的基础上,结合自身对PCM的开发需求,设计制作了基于NIPXI平台的硬件系统和上位机管理软件NI Verstand的HIL仿真测试系统。然后用autoencoder算法对不同驾驶风格的驾驶员采集到的驾驶数据进行驾驶行为标准化,最后用经过标准化后的驾驶数据对HIL系统进行整机测试,证明了该系统的有效性和准确性。

关键词： HIL测试 NI PXI平台 autoencoder 驾驶行为标准化驾驶风格

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