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Application of auto-encoder and Attention Mechanism in Raman Spectroscopy 18th

Application of Auto-encoder and Attention Mechanism in Raman...

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18th International Conference on Intelligent Computing (ICIC)

作者： Bai, Yunyi Xu, Mang Qian, Pengjiang Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9783031138324;9783031138317

Conventional Raman spectroscopy, which is based on the qualitative or quantitative determination of substances, has been widely utilized in industrial manufacture and academic research. However, in traditional Raman spectroscopy, human experience plays a prominent role. Because of the massive amount of comparable information contained in the spectrograms of varying concentration media, the extraction of feature peaks is especially crucial. Although manual feature peak extraction in spectrograms might reduce signal dimensionality to a certain extent, it could also result in spectral information loss, misclassification, and underclassification of feature peaks. This research solves the problem by extracting a feature dimensionality reduction method based on an auto-encoder-attention mechanism, applying a deep learning approach to spectrogram feature extraction, and feeding the features into a neural network for concentration prediction. After rigorous testing, the model's prediction accuracy may reach a unit concentration of 0.01 with a 13% error, providing a reliable aid to manual and timely culture medium replenishment. And through extensive comparison experiments, it is concluded that the self-encoder-based dimensionality reduction method is more accurate compared with the machine learning method. The research demonstrates that using Raman spectroscopy to deep learning can produce positive outcomes and has great potential.

关键词： Raman spectroscopy Deep learning auto-encoder Attentional mechanisms

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A Mobile Network Performance Evaluation Method Based on Multivariate Time Series Clustering with auto-encoder 2018

A Mobile Network Performance Evaluation Method Based on Mult...

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2nd International Conference on Telecommunications and Communication Engineering (ICTCE)

作者： Wang, Xiaoyu Jin, Yuehui Yu, Yanping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Yunnan Univ Finance & Econ Int Language & Culture Inst Kunming Yunnan Peoples R China

ISBN: (纸本)9781450365857

With the extensive use of smartphones, the amount of collected mobile data and types of mobile network performance evaluation indicators are rapidly expanding. This has resulted in a challenge for the mobile network providers of how to assess the mobile network performance quickly and precisely. This paper proposes a mobile network performance evaluation method based on multivariate time series clustering with auto-encoder. In this method, firstly, we remove a part of indicators with high redundancy by combining Pearson's correlation coefficient and lightgbm. Secondly, we consider the changes in indicators over time as multivariate time series, the distance matrices of which are measured by fastDTW and auto-encoder. Finally, the indicators data under different periods are clustered into three categories by using k-medoids on matrices. We apply this method to the indicator data provided by a mobile network provider and discover that the clusters refer to different levels of mobile network performance according to the mobile network provider' KPI standards.

关键词： Multivariate Time Series Mobile Network Performance Evaluation Clustering auto-encoder Distance Measure

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A Convolutional auto-encoder Method for Anomaly Detection on System Logs

A Convolutional Auto-encoder Method for Anomaly Detection on...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Cui, Yu Sun, Yiping Hu, Jinglu Sheng, Gehao Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka Japan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn 800 Dongchuan Rd Shanghai Peoples R China

ISBN: (纸本)9781538666500

Anomaly detection on system logs is to report system failures with utilization of console logs collected from devices, which ensures the reliability of systems. Most previous researches split logs into sequential time windows and regarded each window as an independent instance for classification using popular machine learning methods like support vector machine(SVM), however, neglected the time patterns under logs. Those approaches also suffer from information loss due to the vector representation, and high dimensionality if there is a large number of log events. To make up these deficiencies, unlike most traditional methods that used a vector to represent a period behavior at the macro level, we construct a 2D matrix to reveal more detailed system behaviors in the time period by dividing each window into sequential subwindows. To provide a more efficient representation, we further use the ant colony optimization algorithm to find a highly-coupled event template as the horizontal index of the 2D window matrix to replace the disordered one. To capture time dependencies, a multi-module convolutional auto-encoder is configured as that different paralleled modules scan among different time intervals to extract information respectively. These features are then concatenated in latent space as the final input, which contains diversified time information, for classification by SVM. The experiments on Blue Gene/L log dataset showed that our proposed method outperforms the state-of-art SVM method.

关键词： Log Analysis Anomaly Detection Feature Extraction auto-encoder Ant Colony Optimization

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A Novel Deep Learning Approach: Stacked Evolutionary auto-encoder

A Novel Deep Learning Approach: Stacked Evolutionary Auto-en...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cai, Yaoming Cai, Zhihua Zeng, Meng Liu, Xiaobo Wu, Jia Wang, Guangjun China Univ Geosci Sch Comp Sci Wuhan Peoples R China Qinzhou Univ Qinzhou 535011 Guangxi Peoples R China China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia

ISBN: (纸本)9781509060146

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective Optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification problems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.

关键词： Deep learning auto-encoder Evolutionary Multiobjective Optimization

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Ensemble Clustering via Learning Representations from auto-encoder 15

Ensemble Clustering via Learning Representations from Auto-e...

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15th International Conference on Service Systems and Service Management (ICSSSM)

作者： Wu, Mengqi Liu, Guannan Li, Peng Wu, Junjie Wan, Xin Beihang Univ Sch Econ & Management Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9781538651780

Representation learning is central to clustering analysis and visualization. However, linear dimension reduction methods cannot model high dimensional and sparse feature spaces, thus may neglect some important information during feature representation. In this paper, we focus on developing effective models to capture non-linear and most salient features for clustering analysis, and propose an ensemble framework ECLRA to fuse different layer representations from auto-encoders. To further improve the clustering performance and fully realize the potential of ensemble learning, we also design a strategy to generate enough basic partitions, which turns out to be very effective. Experimental results demonstrate that ECLRA can significantly improve clustering performance compared with baseline methods, and are fairly robust on imbalanced datasets.

关键词： representation learning ensemble clustering auto-encoder text clustering

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Semi-supervised auto-encoder Based Event Detection in Constructing Knowledge Graph for Social Good 19

Semi-supervised Auto-encoder Based Event Detection in Constr...

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19th IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Zhao, Yue Jin, Xiaolong Wang, Yuanzhuo Cheng, Xueqi Univ CAS CAS Key Lab Network Data Sci & Technol Inst Comp Technol CAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781450369343

Knowledge graphs have recently been extensively applied in many different areas (e.g., disaster management and relief, disease diagnosis). For example, event-centric knowledge graphs have been developed to improve decision making in disaster management and relief. This paper focuses on the task of event detection, which is the precondition of event extraction for constructing event-centric knowledge graphs. Event detection identifies trigger words of events in the sentences of a document and further classifies the types of events. It is straightforward that context information is useful for event detection. Therefore, the feature-based methods adopt cross-sentence information. However, they suffer from the complication of human-designed features. On the other hand, the representation-based methods learn document-level embeddings, which, however, contain much noise caused by unsupervised learning. To overcome these problems, in this paper we propose a new model based on Semi-supervised auto-encoder, which learns Context information to Enhance Event Detection, thus called SAE-CEED. This model first applies large-scale unlabeled texts to pre-train an auto-encoder, so that the embeddings of segments learned by the encoder contain the semantic and order information of the original text. It then uses the decoder to extract the context embeddings and fine-tunes them to enhance a bidirectional neural network model to identify event triggers and their types in sentences. Through experiments on the benchmark ACE-2005 dataset, we demonstrate the effectiveness of the proposed SAE-CEED model. In addition, we systematically conduct a series of experiments to verify the impact of different lengths of text segments in the pre-training of the auto-encoder on event detection.

关键词： knowledge graph event detection neural network auto-encoder

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A New Approach for an End-to-end Communication System Using Variational auto-encoder (VAE)

A New Approach for an End-to-end Communication System Using ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Alawad, Mohamad A. Hamdan, Mutasem Q. Hamdi, Khairi A. Foh, Chuan Heng ul Quddus, Atta Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Univ Surrey ICS 5GIC & 6GIC Guildford Surrey England

ISBN: (纸本)9781665435406

In this paper, a new approach has been proposed and investigated with the help of variational auto-encoder (VAE) as a probabilistic model to reconstruct the transmitted symbol without sending the data bits out of the transmitter. The novelty of the proposed End-to-end (E2E) wireless system is in representing the symbol as a image hot vector (IHV) that contains the features of the shape such as spikes, closed squared frame, pixels index location and pixels grey-scale colours. The previously mentioned features are inferred by latent random variables (LRVs). The LRVs are used for fronthaul and backhaul data representation. The LRVs parameters have only been transmitted through the physical wireless channel instead of the original bits as in the classical modulations or the hot vectors in the autoencoders (AE) E2E systems. The new proposed VAE architecture achieved the reconstruction of the symbol from the received LRV. The results show that the VAE with a simple classifier can provide a better symbol error rate (SER) than both AE baseline and classical Hamming code with hard decision decoding, especially at high E-b/N-o.

关键词： Variational auto-encoder Machine Learning auto-encoder Hamming code Latent variable probabilistic models wireless communications Fronthaul Backhaul

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Developing a Hybrid Expert/Data-Driven Health Index for Railway Axleboxes Using auto-encoder Neural Networks

Developing a Hybrid Expert/Data-Driven Health Index for Rail...

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Prognostics and System Health Management Conference in Paris (PHM Paris)

作者： Trilla, Alexandre Janjua, Fand Bermejo, Sergi ALSTOM Serv R&D Barcelona Spain ALSTOM Serv London England

ISBN: (纸本)9781728103297

The railway axlebox is a critical safety component in rolling stock. It bears the weight of the train, and minimises the friction with the rotating axle. Its failure in service might cause derailment. Therefore, its maintenance is decisive. This article presents a methodology to create a standard Health Index that blends the hard threshold-based expert criteria developed at the workshop, with a record of actual vibration data, in order to increase the flexibility of its diagnostic and provide a soft granular feedback. The proposed method makes use of an auto-encoder neural network to accommodate two different approaches: one based on the principal component of a set of vibration features, and the other based on an anomaly detection scheme operating directly at the vibration signal level. The results of the analysis show that the former strategy provides a smooth uncertain index, while the latter yields a sharp sensitive index. Such different diagnosis designs are regarded as an opportunity to better fit the vision of the maintenance course of action.

关键词： railway axlebox health index auto-encoder neural networks principal component anomaly detection

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Situational Estimation of Sports Broadcasting using a Character Level auto-encoder for Live Tweets

Situational Estimation of Sports Broadcasting using a Charac...

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IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Fujimoto, Nodoka Ushiama, Taketoshi Kyushu Univ Grad Sch Design Fukuoka Japan Kyushu Univ Fac Design Fukuoka Japan

ISBN: (纸本)9781665419246

Real-time videos enable people to experience real-world events in real time. However, it is challenging for people to keep watching an event when they are busy and have other activities to carry out. It is therefore crucial to develop a method for supporting the viewers' real-time video viewing effectively. Using sports broadcasting as a representative example of such videos, our goal is to develop a system that detects and automatically estimates the events in sports games that are likely to be of high value to the viewers and notifies them in real time during sports broadcasts. To this end, we propose a general-purpose method that efficiently models situations from small amounts of tweets without using domain-specific knowledge. The effectiveness of the method was verified via experiments by estimating the number of tweets posted from the modeled features of a situation with high accuracy.

关键词： SNS Live Tweet auto-encoder Deep Learning

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Universal auto-encoder Framework for MIMO CSI Feedback

Universal Auto-encoder Framework for MIMO CSI Feedback

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： So, Jinhyun Kwon, Hyukjoon Samsung Semicond Inc San Diego CA 91101 USA

ISBN: (纸本)9798350310900

Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the input and output sizes of the AE are fixed. However, in the real-world scenario, the input and output sizes may vary depending on the number of antennas of the BS and UE and the allocated resource block in the frequency dimension. A naive approach to support the different input and output sizes is to use multiple AE models, which is impractical for the UE due to the limited HW resources. In this paper, we propose a universal AE framework that can support different input sizes and multiple compression ratios. The proposed AE framework significantly reduces the HW complexity while providing comparable performance in terms of compression ratio-distortion trade-off compared to the naive and state-of-the-art approaches.

关键词： MIMO CSI feedback auto-encoder various input size multiple compression ratios

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