检索结果-内蒙古大学图书馆

21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Zhan, Xianghao Lei, Lifeng Kou, Liang Hangzhou Dianzi Univ Coll Comp Sci Hangzhou Peoples R China Hangzhou Dianzi Univ Coll Cyberspace Hangzhou Peoples R China

ISBN: (纸本)9781665478366

In today's power Internet of things environment, power load forecasting has been a research hotspot in recent years. Due to the large and complex data faced by time series forecasting, although the recurrent neural network has strong sequence learning ability, it is still challenging to extract important features and strengthen the generalization ability of the model, In this paper, a power load forecasting model based on encoder-decoder and gated recurrent neural unit (GRU) is proposed, and the attention mechanism layer based on time step is added to the encoder and decoder respectively, so as to mine the characteristics between time sequences adaptively, we discuss and study the different structures of model forecasting. and use RMSE and MAE as evaluation indexes, The simulation results show that the RMSE index of the model is 62%, 18.6% and 16.7% higher than that of the traditional SVR, ARIMA and GRU.

关键词： component Power load forecasting encoder-decoder time attention mechanism GRU structure neural network

来源：评论

学校读者我要写书评

暂无评论

SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Bonada, Jordi Blaauw, Merlijn Univ Pompeu Fabra Mus Technol Grp Barcelona Spain

ISBN: (纸本)9781728176055

We propose a semi-supervised singing synthesizer, which is able to learn new voices from audio data only, without any annotations such as phonetic segmentation. Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder. In a first step, the system is trained in a supervised manner, using a labeled multi-singer dataset. Here, we ensure that the embeddings produced by both encoders are similar, so that we can later use the model with either acoustic or linguistic input features. To learn a new voice in an unsupervised manner, the pretrained acoustic encoder is used to train a decoder for the target singer. Finally, at inference, the pretrained linguistic encoder is used together with the decoder of the new voice, to produce acoustic features from linguistic input. We evaluate our system with a listening test and show that the results are comparable to those obtained with an equivalent supervised approach.

关键词： Singing synthesis semi-supervised encoder-decoder autoregressive convolutional

来源：评论

学校读者我要写书评

暂无评论

Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

引用

WATER 2022年第1期14卷 55-55页

作者： Kusudo, Tsumugu Yamamoto, Atsushi Kimura, Masaomi Matsuno, Yutaka Kindai Univ Fac Agr Dept Environm Management 3327-204 Nakamachi Nara 6318505 Japan

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash-Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.

关键词： reservoir-water level long short-term memory encoder-decoder flood control irrigation water-management tool

来源：评论

学校读者我要写书评

暂无评论

DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

引用

REMOTE SENSING 2022年第21期14卷 5567页

作者： Liu, Zhiheng Chen, Xuemei Zhou, Suiping Yu, Hang Guo, Jianhua Liu, Yanming Xidian Univ Sch Aerosp Sci & Technol Xian 710026 Peoples R China Tech Univ Munich TUM Dept Aerosp & Geodesy Data Sci Earth Observat D-80333 Munich Germany

Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process;(2) dense blocks are used to extract more spatial features to DUPnet's backbone, increasing feature propagation and reuse;(3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.

关键词： encoder-decoder multi-scale spatial pyramid pooling dense connection regression loss remote sensing water body semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Detailed feature extraction network-based fine-grained face segmentation

引用

KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Umirzakova, Sabina Whangbo, Taeg Keun Gachon Univ Dept IT Convergence Engn Seongnam South Korea Gachon Univ Dept Comp Sci Seongnam South Korea

Face parsing refers to the labeling of each facial component in a face image and has been employed in facial stimulation, expression recognition, and makeup use, effectively providing a basis for further analysis, computations, animation, modification, and numerous other applications. Although existing face parsing methods have demonstrated good performance, they fail to extract rich features and recover accurate segmentation maps, particularly for faces with high variations in expression and sufficiently similar appearances. Moreover, these approaches neglect the semantic gaps and dependencies between facial categories and their boundaries. To address these drawbacks, we propose an efficient dilated convolution network with different aspect ratios to attain accurate face parsing of the output by applying the feature extraction capability. The proposed network-structured multiscale dilated encoder-decoder convolution model obtains rich component information and efficiently improves the capture of global information by obtaining low- and high-level semantic features. To achieve a delicate parsing output of the face components along the borders and analyze the connections between the face categories and their border edges, the semantic edge map is learned using a conditional random field, which aims to distinguish border and non-border pixels during the modeling. We conducted experiments using three well-known publicly available face databases. The recorded results demonstrate the high accuracy and capacity of the proposed method in comparison to previous state-of-art methods. Our proposed model achieved a mean accuracy of 90% on the CelebAMask-HQdataset for the category case and 81.43% for the accessory case, and achieved accuracies of 91.58% and 92.44% on the HELEN and LaPa datasets, respectively, thereby demonstrating its effectiveness. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： Face segmentation Dilated convolution Multiscale encoder-decoder Conditional random field

来源：评论

学校读者我要写书评

暂无评论

Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

引用

CHAOS SOLITONS & FRACTALS 2022年 160卷

作者： Coulibaly, Saliya Bessin, Florent Clerc, Marcel G. Mussot, Arnaud Univ Lille CNRS UMR 8523 PhLAM Phys Lasers Atomes & Mol F-59000 Lille France Aston Univ Aston Inst Photon Technol Birmingham B4 7ET England Univ Chile Dept Fis FCFM Casilla 487-3 Santiago Chile Univ Chile Millennium Inst Res Opt FCFM Casilla 487-3 Santiago Chile

Machine learning algorithms have opened a breach in the prediction's fortress of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions makes up a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92% of true positive predictions leading to 60% of *** have implemented a process to forecast extreme events in a fully developed turbulent flow. The novelty of our strategy lies in the information's use theory method to detect precursors and use them as the input of a neural network to infer the incoming extreme events. Our process is suitable for all the extended dissipative systems that can only be partially observed or real-world data.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal Chaos Kerr frequency combs Forecasting extreme events Machine learning Long Short-Term Memory (LSTM) encoder-decoder Transfer entropy

来源：评论

学校读者我要写书评

暂无评论

Learning from what we know: How to perform vulnerability prediction using noisy historical data

引用

EMPIRICAL SOFTWARE ENGINEERING 2022年第7期27卷 169-169页

作者： Garg, Aayush Degiovanni, Renzo Jimenez, Matthieu Cordy, Maxime Papadakis, Mike Le Traon, Yves Univ Luxembourg Dept Comp Sci Fac Sci Technol & Med FSTM Esch Sur Alzette Luxembourg Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Esch Sur Alzette Luxembourg Univ Luxembourg Fac Sci Technol & Med Esch Sur Alzette Luxembourg Univ Luxembourg Esch Sur Alzette Luxembourg

Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the problem, and b) the inherently noisy historical data, i.e., most vulnerabilities are discovered much later than they are introduced. This misleads classifiers as they learn to recognize actual vulnerable components as non-vulnerable. To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed. We perform this by contrasting the known vulnerable, and their respective fixed components. This way, TROVON manages to learn from the things we know, i.e., vulnerabilities, hence reducing the effects of noisy and unbalanced data. We evaluate TROVON by comparing it with existing techniques on three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and Wireshark, with historical vulnerabilities that have been reported in the National Vulnerability Database (NVD). Our evaluation demonstrates that the prediction capability of TROVON significantly outperforms existing vulnerability prediction techniques such as Software Metrics, Imports, Function Calls, Text Mining, Devign, LSTM, and LSTM-RF with an improvement of 40.84% in Matthews Correlation Coefficient (MCC) score under Clean Training Data Settings, and an improvement of 35.52% under Realistic Training Data Settings.

关键词： Vulnerability prediction Trovon Training on vulnerabilities only encoder-decoder Machine translation tf-seq2seq

来源：评论

学校读者我要写书评

暂无评论

DCNet: A Deformable Convolutional Cloud Detection Network for Remote Sensing Imagery

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Liu, Yang Wang, Wen Li, Qingyong Min, Min Yao, Zhigang Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Zhuhai 519082 Peoples R China Beijing Inst Appl Meteorol Beijing 100029 Peoples R China

Recently, deep convolutional neural networks (CNNs) have made important progress in cloud detection with powerful representation learning capability and yield significant performance. However, most existing CNN-based cloud detection methods still face serious challenges because of the variable geometry of clouds and the complexity of underlying surfaces. It is attributed that they only use the fixed grid to extract contextual information, which lacks internal mechanisms to handle the geometric transformations of clouds. To tackle this problem, we propose a deformable convolutional cloud detection network with an encoder-decoder architecture, named DCNet, which can enhance the adaptability of a model to cloud variations. Specifically, we introduce deformable convolution blocks at the encoder to capture saliency spatial contexts adaptively based on the morphological characteristics of clouds and generate high-level semantic representations. After this, we incorporate skip-connection mechanisms into the decoder that integrate low-level spatial contexts as guidance to recover high-level semantic pixel localization and export precise cloud-detection results. Extensive experiments on the GF-1 wide field-of-view (WFV) Satellite Imagery demonstrate that DCNet outperforms several state-of-the-art methods. A public reference implementation of our proposed model in PyTorch is available at shapehttps://***/NiAn-creator/***.

关键词： Clouds Convolution Feature extraction Semantics Satellites Decoding Task analysis Cloud detection deformable convolution block (DCB) encoder-decoder semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning

引用

ENERGIES 2022年第17期15卷 6294页

作者： Li, Mingfei Wu, Jiajian Chen, Zhengpeng Dong, Jiangbo Peng, Zhiping Xiong, Kai Rao, Mumin Chen, Chuangting Li, Xi Sci & Technol Res Inst Co Ltd Guangdong Energy Grp Guangzhou 511466 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Imaging Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Guangdong Huizhou Lng Power Co Ltd Huizhou 516081 Peoples R China Guangdong Energy Grp Co Ltd Guangzhou 510630 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder-decoder LSTM, and encoder-decoder GRU. The results show that for the SOFC test set, the mean square error of encoder-decoder LSTM and encoder-decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder-decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

关键词： solid oxide fuel cell recurrent neural network long short-term memory gated recurrent unit encoder-decoder state prediction

来源：评论

学校读者我要写书评

暂无评论

Abstractive Meeting Summarization based on an Attentional Neural Model 13

Abstractive Meeting Summarization based on an Attentional Ne...

引用

13th International Conference on Machine Vision

作者： Dammak, Nouha BenAyed, Yassine Univ Sousse ISITCom Sousse 4011 Tunisia Univ Sfax MIRACL Lab Sfax 3021 Tunisia

ISBN: (纸本)9781510640412

Through the ages, in all nations, at all times, people spend a lot of their time on discussing new and important issues either on meetings or in conferences. With the evolution and the abundance of Automatic Speech Recognition (ASR) frameworks, automatic transcripts and even automatic meeting summarization are getting more and more interesting. Recently, automatic summarization faces deeper progresses on speech summarization. Neural models had been introduced to tackle with many difficulties of abstractive summarization. Our contribution in this paper focuses on these weaknesses of neural abstractive meeting summarization and suggests an encoder-decoder model based on an attentional algorithm on the decoding sequence. We proposed a deep encoder-decoder model based on attention mechanism (DEDA) for ASR transcripts. Experiments on the AMI Dataset demonstrates that our proposed method ensured competitive results with the state of the art even on extractive or abstractive models. The experimental analyses also put the stress on the performance of the summarized utterances as well as the reduction of the occurrence repetition in summaries.

关键词： Summarization meetings deep learning encoder-decoder LSTM RNN attention model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：