% Пример bib-записи для статьи в русскоязычном журнале.
@article{Zhang2017,
   abstract = {Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.},
   author = {Ran Zhang and Zhen Peng and Lifeng Wu and Beibei Yao and Yong Guan},
   doi = {10.3390/S17030549},
   isbn = {8613401108644},
   issn = {14248220},
   issue = {3},
   journal = {Sensors 2017, Vol. 17, Page 549},
   keywords = {deep neural networks,faults diagnosis,raw sensor data,temporal coherence},
   month = {3},
   pages = {549},
   pmid = {28282936},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence},
   volume = {17},
   url = {https://www.mdpi.com/1424-8220/17/3/549/htm https://www.mdpi.com/1424-8220/17/3/549},
   year = {2017},
}
@article{Li2020,
   abstract = {Rolling element bearings are widely used in rotating machinery and, at the same time, they are easily damaged due to harsh operating environments and conditions. As a result, rolling element bearings are critical to the safe operation of the mechanical devices. The incipient fault information extraction of rolling bearings mainly faces the following difficulties: (1) The fault signal is too weak. (2) The fault mechanism and the dynamic model of the rolling bearing system are complex. (3) The oscillations caused by the fault shocks are overlapped due to the smaller impact between two adjacent faults. (4) The impact interval of the fault will change randomly. To overcome the aforementioned difficulties, a connection network constructed by resonance-based sparse signal decomposition (RSSD) and broad learning system (BLS) without the need for deep architecture, namely RSSD-BLS, is proposed for intelligent fault diagnosis. We construct RSSD-BLS by input layer, RSSD decomposition layer, feature layer and output layer. So, when the observed vibration signals are the input layer, the network first uses RSSD to decompose the raw vibration signal into high resonance components and low resonance components. Then, the network obtains energy spectrum features of high resonance components which decomposed by RSSD to extract the unique features in the feature. Finally, the network recognizes different fault conditions in the output layer. Through comparing with commonly used intelligent network diagnosis method, the superiority of the proposed RSSD-BLS is verified.},
   author = {Xiaocheng Li and Jingcheng Wang and Bin Zhang},
   doi = {10.1177/0142331219864820},
   issn = {14770369},
   issue = {2},
   journal = {Transactions of the Institute of Measurement and Control},
   keywords = {Rolling element bearing,broad learning system,fault diagnosis,feature execration,incipient fault,resonance-based sparse signal decomposition},
   month = {1},
   pages = {169-179},
   publisher = {SAGE Publications Ltd},
   title = {Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network},
   volume = {42},
   year = {2020},
}

@article{Zhang2017a,
   abstract = {Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.},
   author = {Wei Zhang and Gaoliang Peng and Chuanhao Li and Yuanhang Chen and Zhujun Zhang},
   doi = {10.3390/s17020425},
   issn = {1424-8220},
   issue = {2},
   journal = {Sensors},
   keywords = {anti,convolutional neural networks,domain adaptation,intelligent fault diagnosis,noise},
   month = {2},
   pages = {425},
   pmid = {28241451},
   publisher = {Multidisciplinary Digital Publishing Institute},
   title = {A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals},
   volume = {17},
   url = {https://www.mdpi.com/1424-8220/17/2/425/htm https://www.mdpi.com/1424-8220/17/2/425 http://www.mdpi.com/1424-8220/17/2/425},
   year = {2017},
}

@article{Chen2020,
   abstract = {Due to the advantage of automatically extracting features from raw data, deep learning (DL) has been increasingly favored in the field of machine fault diagnosis. However, DL exposes the problems of large sample size and long training time, and in actual working conditions, the amount of labeled fault data available is relatively small, so a DL model of good generalization and high accuracy is difficult to be trained. In order to solve these problems, a deep transfer convolutional neural network (DTCNN) is proposed in this research. ResNet-50 is selected as the pre-trained model of deep convolutional neural network, and is transferred to solve the problem of bearing fault classification based on the idea of transfer learning. Firstly, raw fault signals are converted into time-frequency images by using continuous wavelet transform (CWT). Then, the images are further converted into RGB formats, which are used as the input of DTCNN. Finally, an end-to-end fault diagnosis model based on DTCNN is designed. The proposed method is validated on two datasets collected from motor bearing and self-priming centrifugal pump, respectively. Most sub-datasets from motor bearing show the prediction accuracies near 100%, and in the self-priming centrifugal pump dataset, we achieve improvement in accuracy from 99.48%±0.1966 to 99.98%±0.0332. The experimental results demonstrate that the proposed method outperforms other DL methods and traditional machine-learning methods.},
   author = {Zhihao Chen and Jian Cen and Jianbin Xiong},
   doi = {10.1109/ACCESS.2020.3016888},
   issn = {2169-3536},
   journal = {IEEE Access},
   keywords = {Fault diagnosis,continuous wavelet transform,convolutional neural network,rolling bearing,transfer learning},
   pages = {150248-150261},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network},
   volume = {8},
   url = {https://ieeexplore.ieee.org/document/9167237/},
   year = {2020},
}

@article{Xu2020,
   abstract = {Machine learning techniques have been successfully applied in intelligent fault diagnosis of rolling bearings in recent years. However, in the real world industrial application, the dissimilarity of data due to changes in the working conditions and data acquisition environment often cause a poor performance of the existing fault diagnosis methods. Consequently, to address these inadequacies, this paper developed a novel method by integrating the Convolutional Neural Networks (CNNs) with the Variational Mode Decomposition (VMD) algorithms. Named as “Variational Mode Decomposition with Deep Convolutional Neural Networks (VMD-DCNNs)”, the method, in an end-to-end way, directly processes raw vibration signals without artificial experiences and manual intervention to realize the fault diagnosis of rolling bearings. In addition, the CNN technique is used to extract features from each Intrinsic Mode Function (IMF) in order to address the deficiency in extracting features from a single source and to achieve an effective and efficient fault diagnosis of rolling bearings under different environments and states. The value of parameter K of the VMD-DCNNs model is optimized by considering time complexity and generalization ability of the model. Lastly, bearing experiments are conducted to verify the superiority of the VMD-DCNNs in diagnosing fault under different conditions. The visualizations of the signals in the convolutional layer explain the reasonability in selecting the value of parameter K and they also indicate that the translational invariances in a raw IMF component have been learned by the VMD-DCNNs model.},
   author = {Zifei Xu and Chun Li and Yang Yang},
   doi = {10.1016/J.ASOC.2020.106515},
   issn = {1568-4946},
   journal = {Applied Soft Computing},
   keywords = {Convolutional Neural Networks,Deep learning,Fault diagnosis,Rolling bearing,VariationaL Mode Decomposition},
   month = {10},
   pages = {106515},
   publisher = {Elsevier},
   title = {Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks},
   volume = {95},
   year = {2020},
}

@article{Liu2018,
   abstract = {As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy.},
   author = {Han Liu and Jianzhong Zhou and Yang Zheng and Wei Jiang and Yuncheng Zhang},
   doi = {10.1016/J.ISATRA.2018.04.005},
   issn = {1879-2022},
   journal = {ISA transactions},
   keywords = {Han Liu,Jianzhong Zhou,MEDLINE,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,PubMed Abstract,Yuncheng Zhang,doi:10.1016/j.isatra.2018.04.005,pmid:29681393},
   month = {6},
   pages = {167-178},
   pmid = {29681393},
   publisher = {ISA Trans},
   title = {Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders},
   volume = {77},
   url = {https://pubmed.ncbi.nlm.nih.gov/29681393/},
   year = {2018},
}

@article{Shao2017,
   abstract = {A deep autoencoder feature learning method is proposed.•Maximum correntropy is used to design the new deep autoencoder loss function.•Artificial fish swarm algorithm is used to optimize parameters.},
   author = {Haidong Shao and Hongkai Jiang and Huiwei Zhao and Fuan Wang},
   doi = {10.1016/J.YMSSP.2017.03.034},
   issn = {0888-3270},
   issue = {95},
   journal = {Mechanical Systems and Signal Processing},
   keywords = {Artificial fish swarm algorithm,Deep autoencoder,Fault diagnosis,Feature learning,Maximum correntropy},
   month = {10},
   pages = {187-204},
   publisher = {Academic Press},
   title = {A novel deep autoencoder feature learning method for rotating machinery fault diagnosis},
   volume = {C},
   url = {https://www.infona.pl//resource/bwmeta1.element.elsevier-b256d4b3-299c-3168-bba0-924b6fd59484},
   year = {2017},
}

@inproceedings{liang2019intelligent,
   author = {Pengfei Liang and Chao Deng and Jun Wu and Zhixin Yang and Jinxuan Zhu},
   doi = {10.1109/ICPHM.2019.8819444},
   isbn = {978-1-5386-8357-6},
   institution = {IEEE},
   journal = {2019 IEEE International Conference on Prognostics and Health Management (ICPHM)},
   month = {6},
   pages = {1-5},
   publisher = {IEEE},
   title = {Intelligent Fault Diagnosis of Rolling Element Bearing Based on Convolutional Neural Network and Frequency Spectrograms},
   url = {https://ieeexplore.ieee.org/document/8819444/},
   year = {2019},
}

@inproceedings{yuan2018intelligent,
   author = {Zhuang Yuan and Laibin Zhang and Lixiang Duan and Tao Li},
   institution = {IEEE},
   journal = {2018 Prognostics and System Health Management Conference (PHM-Chongqing)},
   pages = {292-296},
   title = {Intelligent fault diagnosis of rolling element bearings based on HHT and CNN},
   year = {2018},
}
@article{lei2013review,
   author = {Yaguo Lei and Jing Lin and Zhengjia He and Ming J Zuo},
   doi = {10.1016/j.ymssp.2012.09.015},
   issn = {08883270},
   issue = {1-2},
   journal = {Mechanical Systems and Signal Processing},
   month = {2},
   pages = {108-126},
   publisher = {Elsevier},
   title = {A review on empirical mode decomposition in fault diagnosis of rotating machinery},
   volume = {35},
   url = {https://www.sciencedirect.com/science/article/pii/S0888327012003731 https://linkinghub.elsevier.com/retrieve/pii/S0888327012003731},
   year = {2013},
}
@article{luo2003line,
   author = {G Y Luo and D Osypiw and M Irle},
   issue = {8},
   journal = {Journal of vibration and control},
   pages = {931-947},
   publisher = {Sage Publications},
   title = {On-line vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing},
   volume = {9},
   year = {2003},
}

@article{wang1993early,
   author = {W.J. Wang and P.D. McFadden},
   doi = {10.1006/mssp.1993.1008},
   issn = {08883270},
   issue = {3},
   journal = {Mechanical Systems and Signal Processing},
   month = {5},
   pages = {193-203},
   publisher = {Elsevier},
   title = {Early detection of gear failure by vibration analysis i. calculation of the time-frequency distribution},
   volume = {7},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S0888327083710083},
   year = {1993},
}
@article{zhen2008bearing,
   author = {Li Zhen and He Zhengjia and Zi Yanyang and Chen Xuefeng},
   doi = {10.1016/j.matcom.2007.12.004},
   issn = {03784754},
   issue = {3},
   journal = {Mathematics and Computers in Simulation},
   month = {12},
   pages = {318-338},
   publisher = {Elsevier},
   title = {Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme},
   volume = {79},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S0378475408000037},
   year = {2008},
}

@article{wang2015enhanced,
   author = {Wilson Wang and Fathy Ismail and others},
   issue = {10},
   journal = {IEEE Transactions on Instrumentation and Measurement},
   pages = {2679-2687},
   publisher = {IEEE},
   title = {An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring},
   volume = {64},
   year = {2015},
   doi = {10.1109/TIM.2015.2419031}
}

@online{mnmWeb001,
   title = {Prognostics Center of Excellence - Data Repository},
   url = {https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing},
   urldate = {2022-01-21},
   language = {English}

}

@article{Hamad2019,
   abstract = {Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.},
   author = {Rebeen Hamad and Eric Jarpe and Jens Lundstrom},
   doi = {10.1109/SMC.2018.00318},
   isbn = {9781538666500},
   journal = {Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018},
   month = {1},
   pages = {1839-1845},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data},
   year = {2019},
}

@article{Yu2017,
   author = {Xiao Yu and Fei Dong and Enjie Ding and Shoupeng Wu and Chunyang Fan},
   doi = {10.1109/ACCESS.2017.2773460},
   issn = {2169-3536},
   journal = {IEEE Access},
   pages = {3715-3730},
   publisher = {IEEE},
   title = {Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection},
   volume = {6},
   url = {http://ieeexplore.ieee.org/document/8116607/},
   year = {2018},
}
@article{Han2021,
   author = {Haoran Han and Huan Wang and Zhiliang Liu and Jiayi Wang},
   doi = {10.1016/j.isatra.2021.04.022},
   issn = {0019-0578},
   journal = {ISA Transactions},
   month = {3},
   pages = {13-23},
   publisher = {Elsevier},
   title = {Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings},
   volume = {122},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S0019057821002196},
   year = {2022},
}
@article{Xu2021,
   author = {Yang Xu and Zhixiong Li and Shuqing Wang and Weihua Li and Thompson Sarkodie-Gyan and Shizhe Feng},
   doi = {10.1016/j.measurement.2020.108502},
   issn = {02632241},
   journal = {Measurement},
   month = {2},
   pages = {108502},
   publisher = {Elsevier},
   title = {A hybrid deep-learning model for fault diagnosis of rolling bearings},
   volume = {169},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S0263224120310307},
   year = {2021},
}

@article{EpishevIMMSSZE13,
	author    = {Виталий Владимирович Епишев and
	Александр Петрович Исаев and
	Руслан Марсович Миниахметов and
	Александр Вячеславович Мовчан and
	Алексей Сергеевич Смирнов and
	Леонид Борисович Соколинский and
	Михаил Леонидович Цымблер and
	Вадим Викторович Эрлих},
	title     = {Система интеллектуального анализа данных физиологических исследований в спорте высших достижений},
	journal   = {Вестник ЮУрГУ. Серия: Вычислительная математика и информатика},
	volume    = {2},
	number    = {1},
	pages     = {44--54},
	year      = {2013},
	url		  = {https://doi.org/10.14529/cmse130105},
	doi       = {10.14529/cmse130105},
	language  = {russian}
}

% Пример bib-записи для статьи в англоязычном журнале.
@article{DBLP:journals/pcs/LevshinM09,
	author    = {Dmitry V. Levshin and A. S. Markov},
	title     = {Algorithms for integrating PostgreSQL with the semantic web},
	journal   = {Program. Comput. Softw.},
	volume    = {35},
	number    = {3},
	pages     = {136--144},
	year      = {2009},
	url       = {https://doi.org/10.1134/S0361768809030025},
	doi       = {10.1134/S0361768809030025},
	timestamp = {Sun, 06 Sep 2020 18:04:07 +0200},
	biburl    = {https://dblp.org/rec/journals/pcs/LevshinM09.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Пример bib-записи статьи в arXiv.
@article{DBLP:journals/corr/abs-2008-12256,
	author    = {Leonid B. Sokolinsky},
	title     = {BSF-skeleton: user manual},
	journal   = {CoRR},
	volume    = {abs/2008.12256},
	year      = {2020},
	url       = {https://arxiv.org/abs/2008.12256},
	eprinttype = {arXiv},
	eprint    = {2008.12256},
	timestamp = {Tue, 15 Sep 2020 20:52:22 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2008-12256.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org},
}

% Пример bib-записи статьи в серии сборников трудов.
@inproceedings{ChetverushkinYKS19,
	author    = {Chetverushkin, Boris N. and
	Yakobovskiy, Mikhail V. and
	Kornilina, Marina A. and
	Semenova, Alena V.},
	editor    = {Sokolinsky, Leonid and
	Zymbler, Mikhail},
	title     = {Numerical Algorithms for {HPC} Systems and Fault Tolerance},
	booktitle = {Parallel Computational Technologies},
	series    = {Communications in Computer and Information Science},
	volume    = {1063},
	year      = {2019},
	publisher = {Springer},
	address   = {Cham},
	pages     = {34--44},
	doi       = {10.1007/978-3-030-28163-2_3},
	isbn      = {978-3-030-28163-2}
}

% Пример bib-записи статьи в русскоязычных трудах конференции.
@inproceedings{AkimovaB10,
	author    = {Елена Николаевна Акимова and Дмитрий Владимирович Белоусов},
	title     = {Распараллеливание решения линейной обратной задачи на МВС-1000 и графических процессорах},
	booktitle = {Параллельные вычислительные технологии (ПаВТ’2010): Труды международной научной конференции, Уфа, 29~марта~-- 2~апреля, 2010},
	pages     = {18--27},
	publisher = {Издательский центр ЮУрГУ},
	address   = {Челябинск},
	year      = {2010},
	language  = {russian}
}

% Пример bib-записи статьи в англоязычных трудах конференции.
@inproceedings{DBLP:conf/npc/LiuWZGL12,
	author    = {Gangfeng Liu and
	Yunlan Wang and
	Tianhai Zhao and
	Jianhua Gu and
	Dongyang Li},
	editor    = {James J. Park and
	Albert Y. Zomaya and
	Sang{-}Soo Yeo and
	Sartaj Sahni},
	title     = {mHLogGP: {A} Parallel Computation Model for {CPU/GPU} Heterogeneous Computing Cluster},
	booktitle = {Network and Parallel Computing, 9th {IFIP} International Conference, {NPC} 2012, Gwangju, Korea, September 6--8, 2012. Proceedings},
	series    = {Lecture Notes in Computer Science},
	volume    = {7513},
	pages     = {217--224},
	publisher = {Springer},
	year      = {2012},
	url       = {https://doi.org/10.1007/978-3-642-35606-3_25},
	doi       = {10.1007/978-3-642-35606-3_25},
	timestamp = {Tue, 14 May 2019 10:00:51 +0200},
	biburl    = {https://dblp.org/rec/conf/npc/LiuWZGL12.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Пример bib-записи сборника трудов конференции.
@proceedings{DBLP:conf/eforensics/2010,
	editor    = {Xuejia Lai and
	Dawu Gu and
	Bo Jin and
	Yongquan Wang and
	Hui Li},
	title     = {Forensics in Telecommunications, Information, and Multimedia - Third International {ICST} Conference, e-Forensics 2010, Shanghai, China, November 11--12, 2010, Revised Selected Papers},
	series    = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
	volume    = {56},
	publisher = {Springer},
	year      = {2011},
	url       = {https://doi.org/10.1007/978-3-642-23602-0},
	doi       = {10.1007/978-3-642-23602-0},
	isbn      = {978-3-642-23601-3},
	timestamp = {Fri, 26 May 2017 00:48:49 +0200},
	biburl    = {https://dblp.org/rec/conf/eforensics/2010.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Пример bib-записи раздела книги с собственным названием.
@incollection{DBLP:books/sp/byeon2010/RiedelSMWL10,
	author    = {Morris Riedel and
	Achim Streit and
	Daniel Mallmann and
	Felix Wolf and
	Thomas Lippert},
	editor    = {Ok{-}Hwan Byeon and
	Jang Hyuk Kwon and
	Thom H. Dunning and
	Kumwon Cho and
	Aurore Savoy{-}Navarro},
	title     = {Experiences and Requirements for Interoperability Between {HTC} and {HPC}-driven e-Science Infrastructure},
	booktitle = {Future Application and Middleware Technology on e-Science},
	pages     = {113--123},
	publisher = {Springer},
	year      = {2010},
	url       = {https://doi.org/10.1007/978-1-4419-1719-5_12},
	doi       = {10.1007/978-1-4419-1719-5_12},
	timestamp = {Wed, 25 Oct 2017 12:49:18 +0200},
	biburl    = {https://dblp.org/rec/books/sp/byeon2010/RiedelSMWL10.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Пример bib-записи главы книги.
@inbook{WinskelN95,
	author    = {Winskel, Glynn and Nielsen, Mogens},
	title     = {Models for Concurrency},
	year      = {1995},
	isbn      = {0198537808},
	publisher = {Oxford University Press, Inc.},
	address   = {USA},
	booktitle = {Handbook of Logic in Computer Science (Vol. 4): Semantic Modelling},
	pages     = {1--148},
	numpages  = {148},
	url       = {https://doi.org/10.5555/218623.218630},
	doi       = {10.5555/218623.218630}
}

% Пример bib-записи русскоязычной книги.
@book{Eremin09,
	author    = {Иван Иванович Ерёмин},
	title     = {Фейеровские методы для задач выпуклой и линейной оптимизации},
	publisher = {Изд-во ЮУрГУ},
	address   = {Челябинск},
	year      = {2009},
	pages	  = {200},
	language  = {russian}
}

% Пример bib-записи англоязычной книги.
@book{YangZDP20,
	author    = {Yang, Qiang and
	Zhang, Yu and
	Dai, Wenyuan and
	Pan, Sinno Jialin},
	title     = {Transfer Learning},
	publisher = {Cambridge University Press},
	address   = {Cambridge},
	year      = {2020},
	pages	  = {390},
	doi       = {10.1017/9781139061773}
}

% Пример bib-записи русскоязычного источника в Интернете.
@online{Levin,
	author    = {Владимир Константинович Левин},
	title     = {Отечественные суперкомпьютеры семейства МВС},
	url       = {http://parallel.ru/mvs/levin.html},
	urldate   = {2012-05-27},
	language  = {russian}
}

% Пример bib-записи англоязычного источника в Интернете.
@online{Harkins08,
	author    = {D. Harkins},
	title     = {Synthetic Initialization Vector (SIV) Authenticated Encryption Using the Advanced Encryption Standard (AES)},
	year      = {2008},
	url 	  = {http://tools.ietf.org/html/rfc5297},
	urldate   = {2012-05-27},
	language  = {russian}
}

% Пример bib-записи технического отчета.
@techreport{PageBMW99,
	number    = {1999-66},
	author    = {Lawrence Page and
	Sergey Brin and
	Rajeev Motwani and
	Terry Winograd},
	title     = {The PageRank Citation Ranking: Bringing Order to the Web},
	type      = {Technical Report},
	year      = {1999},
	institution = {Stanford InfoLab},
	url       = {http://ilpubs.stanford.edu:8090/422/}
}

% Пример bib-записи диссертации.
@phdthesis{DBLP:phd/dnb/Vasquez19,
	author    = {Leonardo Solis{-}Vasquez},
	title     = {Accelerating Molecular Docking by Parallelized Heterogeneous Computing - {A} Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs},
	school    = {Darmstadt University of Technology, Germany},
	year      = {2019},
	url       = {http://tuprints.ulb.tu-darmstadt.de/9288/},
	timestamp = {Sat, 17 Jul 2021 09:03:01 +0200},
	biburl    = {https://dblp.org/rec/phd/dnb/Vasquez19.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}
