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Yingying Guan - IEEE Xplore Author Profile

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Author details

Yingying Guan

Publications

11

Citations

33

Publications by Year

20162025

Co-Authors:

Tao ChenZheng ChuLei GuoPengxing GuoZhongshi He

Show All Co-Authors (34)

Yingying Guan

Affiliation

School of Computer Science and Engineering

Northeastern University

Shenyang, China

Publication Topics

Optimization Problem,
Time Slot,
Additive Noise,
Internet Of Things,
Objective Function,
Phase Shift,
Bandwidth Allocation,
Beamforming Vector,
Computational Complexity,
Intelligent Reflecting Surface,
Internet Of Things Devices,
Lagrangian Method

Biography

Yingying Guan (Graduate Student Member, IEEE) received the M.S. degree in communication and information systems from Northeastern University, Shenyang, China, in 2017, where she is currently pursuing the Ph.D. degree.,From 2019 to 2020, she was a Visiting Researcher with University of Exeter, Exeter, U.K. Her current research interests include network slicing, edge computing, and resource management.(Based on document published on 1 July 2024).

Publications

11

Citations

33

Publications by Year

20162025

Co-Authors:

Tao Chen
Zheng Chu
Lei Guo
Pengxing Guo
Zhongshi He

Show All Co-Authors (34)

Author's Published Works

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Showing 1-25 of 2,075 resultsfor "Index Terms":Incomplete Process

Conferences (1,322)

Journals (726)

Magazines (18)

Early Access Articles (9)

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TsCDD-GAN: A Conditional Dual-Discriminator Generative Adversarial Network for Incomplete Time Series Data Imputation and Clustering

Bo Peng;Chen Dong

2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)

Year: 2024 | Conference Paper |

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Time series clustering is a complex unsupervised data mining and analysis technique that can be applied to various fields such as signal processing, financial analysis, and more. However, time series data often contain missing values due to reasons such as sensor failures, data corruption, or intentional omissions in the real world. Traditional generative imputation methods operate independently f...Show More

TsCDD-GAN: A Conditional Dual-Discriminator Generative Adversarial Network for Incomplete Time Series Data Imputation and Clustering

Bo Peng;Chen Dong

2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)

Year: 2024 | Conference Paper |

A Note on Incomplete Information Modeling in the Evidence Theory

Xuelian Zhou;Yongchuan Tang

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (10)

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Incomplete information exists widely in practical engineering. How to model the incomplete information in the Dempster-Shafer evidence theory (DST) framework is still an open issue. In this paper, we present a framework on incomplete information processing in DST. We extend the model of uncertain information from the closed world assumption to the open world assumption where the incomplete informa...Show More

A Note on Incomplete Information Modeling in the Evidence Theory

Xuelian Zhou;Yongchuan Tang

Year: 2019 | Volume: 7 | Journal Article |

Parallel and Progressive Approaches for Skyline Query Over Probabilistic Incomplete Database

Yifu Zeng;Kenli Li;Shui Yu;Yantao Zhou;Keqin Li

Year: 2018 | Volume: 6 | Journal Article |

Cited by: Papers (10)

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The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary ...Show More

Parallel and Progressive Approaches for Skyline Query Over Probabilistic Incomplete Database

Yifu Zeng;Kenli Li;Shui Yu;Yantao Zhou;Keqin Li

Year: 2018 | Volume: 6 | Journal Article |

Top-k Dominating Queries on Incomplete Data

Xiaoye Miao;Yunjun Gao;Baihua Zheng;Gang Chen;Huiyong Cui

IEEE Transactions on Knowledge and Data Engineering

Year: 2016 | Volume: 28, Issue: 1 | Journal Article |

Cited by: Papers (50)

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The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first t...Show More

Top-k Dominating Queries on Incomplete Data

Xiaoye Miao;Yunjun Gao;Baihua Zheng;Gang Chen;Huiyong Cui

IEEE Transactions on Knowledge and Data Engineering

Year: 2016 | Volume: 28, Issue: 1 | Journal Article |

An Effective Imputation Technique for Improving the Performance of Skyline Queries for Incomplete Database

S. Deepa Kanmani;E. Kirubakaran;R. Elijah Blessing Vinoth;A. Shamila Ebenezer

2019 International Conference on Data Science and Communication (IconDSC)

Year: 2019 | Conference Paper |

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Skyline query is one of the types of user preference-based query which is used to retrieve non-dominated results. This is extensively used in applications like recommender system, decision-making applications and e-commerce. Existing traditional skyline algorithms are well suited only for complete data. However, we find in the real time dataset we come across one or more missing data and this will...Show More

An Effective Imputation Technique for Improving the Performance of Skyline Queries for Incomplete Database

S. Deepa Kanmani;E. Kirubakaran;R. Elijah Blessing Vinoth;A. Shamila Ebenezer

2019 International Conference on Data Science and Communication (IconDSC)

Year: 2019 | Conference Paper |

Imputation Using Machine Learning Techniques

R Sivakani;Gufran Ahmad Ansari

2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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In today's environment to find the missing value in the data set has become the biggest challenge for the industry people, scientists, academicians and for the researchers. With the incomplete dataset we are not able to apply algorithm to find the result. If the dataset is not complete then only we can apply algorithms and then result can be analyzed to get the efficient output. In this paper we h...Show More

Imputation Using Machine Learning Techniques

R Sivakani;Gufran Ahmad Ansari

2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP)

Year: 2020 | Conference Paper |

Streaming Process Discovery Method for Semi-Structured Business Processes

Anatoliy Batyuk;Volodymyr Voityshyn

2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP)

Year: 2020 | Conference Paper |

Cited by: Papers (4)

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A streaming process discovery method for semi-structured business processes is proposed. The method is further development of the Fuzzy Miner which originally was intended for stationary event data sets. The streaming Fuzzy Miner supports concept drift of a business process model so that recent events are considered more important as the earlier ones. The method was used to represent business proc...Show More

Streaming Process Discovery Method for Semi-Structured Business Processes

Anatoliy Batyuk;Volodymyr Voityshyn

2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP)

Year: 2020 | Conference Paper |

A Gaussian Latent Variable Model for Incomplete Mixed Type Data

Marzieh Ajirak;Petar M. Djurić

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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In many machine learning problems, one has to work with data of different types, including continuous, discrete, and categorical data. Further, it is often the case that many of these data are missing from the database. This paper proposes a Gaussian process framework that efficiently captures the information from mixed numerical and categorical data that effectively incorporates missing variables...Show More

A Gaussian Latent Variable Model for Incomplete Mixed Type Data

Marzieh Ajirak;Petar M. Djurić

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Bayesian Network-based Technical Index Estimation for Industrial Flotation Process under Incomplete Data

Hao Yan;Jin Zhu;Fuli Wang;Dakuo He;Qingkai Wang

2020 Chinese Control And Decision Conference (CCDC)

Year: 2020 | Conference Paper |

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Due to the lack of detection instruments or long measurement cycles in the industrial flotation process, accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational adjustment. In the real-world flotation process, incomplete data is a widespread phenomenon owing to hardware sensor failures and other reasons. To this end, th...Show More

Bayesian Network-based Technical Index Estimation for Industrial Flotation Process under Incomplete Data

Hao Yan;Jin Zhu;Fuli Wang;Dakuo He;Qingkai Wang

2020 Chinese Control And Decision Conference (CCDC)

Year: 2020 | Conference Paper |

Incomplete Multi-View Clustering Based on Weighted Adaptive Graph Learning

Kaiwu Zhang;Jinmei Song;Yao Yu;Shiqiang Du

2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Multi-view clustering methods utilize complementary and consistent information among different views to classify samples into correct clusters. However, traditional multi-view clustering methods are proposed based on complete datasets. In practical applications, complete data samples rarely exist, and incomplete data are more common. This paper proposes an incomplete multi-view clustering algorith...Show More

Incomplete Multi-View Clustering Based on Weighted Adaptive Graph Learning

Kaiwu Zhang;Jinmei Song;Yao Yu;Shiqiang Du

2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2022 | Conference Paper |

Modeling and Computing Probabilistic Skyline on Incomplete Data

Kaiqi Zhang;Hong Gao;Xixian Han;Zhipeng Cai;Jianzhong Li

IEEE Transactions on Knowledge and Data Engineering

Year: 2020 | Volume: 32, Issue: 7 | Journal Article |

Cited by: Papers (17)

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The skyline query is important in the database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references. In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each po...Show More

Modeling and Computing Probabilistic Skyline on Incomplete Data

Kaiqi Zhang;Hong Gao;Xixian Han;Zhipeng Cai;Jianzhong Li

IEEE Transactions on Knowledge and Data Engineering

Year: 2020 | Volume: 32, Issue: 7 | Journal Article |

Data analysis on incomplete failure of ship electromechanical system based on bayesian method

Qingzhi Meng;Yanling Qian;Lei Li;Long Wang

2017 Prognostics and System Health Management Conference (PHM-Harbin)

Year: 2017 | Conference Paper |

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As a result of the influence of man-made or other factors, the recorded fault data in the process of keeping maintenance of ship electromechanical system are often inaccurate with incomplete record information, low-quality data and small size sample. For the incomplete failure data, this paper will analyze the failure data of the certain type of ship electromechanical system thoroughly and compreh...Show More

Data analysis on incomplete failure of ship electromechanical system based on bayesian method

Qingzhi Meng;Yanling Qian;Lei Li;Long Wang

2017 Prognostics and System Health Management Conference (PHM-Harbin)

Year: 2017 | Conference Paper |

Bayesian estimation of a power law process with incomplete data

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Journal of Systems Engineering and Electronics

Year: 2021 | Volume: 32, Issue: 1 | Journal Article |

Cited by: Papers (1)

Due to the simplicity and flexibility of the power law process, it is widely used to model the failures of repairable systems. Although statistical inference on the parameters of the power law process has been well developed, numerous studies largely depend on complete failure data. A few methods on incomplete data are reported to process such data, but they are limited to their specific cases, es...Show More

Bayesian estimation of a power law process with incomplete data

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Journal of Systems Engineering and Electronics

Year: 2021 | Volume: 32, Issue: 1 | Journal Article |

Multivariate Hawkes Processes for Incomplete Biased Data

Zihan Zhou;Mingxuan Sun

2021 IEEE International Conference on Big Data (Big Data)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Multivariate Hawkes processes have been widely used in many applications such as crime detection and disaster rescue forecast to model events that exhibit self-exciting properties. One of the biggest challenges is that data collected from real world is usually incomplete, and even biased. The training of a machine learning model using such data can introduce biased predictions. For example, event ...Show More

Multivariate Hawkes Processes for Incomplete Biased Data

Zihan Zhou;Mingxuan Sun

2021 IEEE International Conference on Big Data (Big Data)

Year: 2021 | Conference Paper |

CIDS: An Efficient Algorithm for Processing Skyline Queries for Partially Complete Data in Cloud Environment

Yonis Gulzar;Ali A. Alwan

Year: 2022 | Volume: 10 | Journal Article |

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From a set of existing tuples, a skyline operator retrieves only a subset, superior tuples that are of a person’s interest and are non-dominant. Processing of queries using the skyline operator is an expensive and exhaustive task. It gets more complicated when skyline queries are applied on partially complete data and databases are distributed over different data centers. Incompleteness in data ra...Show More

CIDS: An Efficient Algorithm for Processing Skyline Queries for Partially Complete Data in Cloud Environment

Yonis Gulzar;Ali A. Alwan

Year: 2022 | Volume: 10 | Journal Article |

Incomplete data fuzzy C-means method based on spatial distance of sample

Jingyi Cao;Chongquan Zhong;Dan Li

2019 Chinese Control Conference (CCC)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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In this paper, an incomplete data fuzzy C-means method based on spatial distance of sample(FCM-SDS) is proposed. The method uses the nearest neighbor rule to utilize the spatial distribution information of the sample to obtain the missing attribute information of the incomplete data to interpolate the incomplete data set. Combining the nearest neighbor samples, the spatial distance with the adjust...Show More

Incomplete data fuzzy C-means method based on spatial distance of sample

Jingyi Cao;Chongquan Zhong;Dan Li

2019 Chinese Control Conference (CCC)

Year: 2019 | Conference Paper |

Linear Prediction of Discrete-Time $1/f$ Processes

Siamak Yousefi;Joakim Jaldén;Thomas Eriksson

IEEE Signal Processing Letters

Year: 2010 | Volume: 17, Issue: 11 | Journal Article |

Cited by: Papers (5)

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In this letter, the linear predictability of discrete-time stationary stochastic processes with 1/|f|α-shaped power spectral density (PSD) is considered. In particular, the spectral flatness measure (SFM)-which yields a lower bound for the normalized mean-squared-error (NMSE) of any linear one-step-ahead (OSA) predictor-is obtained analytically as a function of α ∈ [0, 1]. By comparing the SFM bou...Show More

Linear Prediction of Discrete-Time $1/f$ Processes

Siamak Yousefi;Joakim Jaldén;Thomas Eriksson

IEEE Signal Processing Letters

Year: 2010 | Volume: 17, Issue: 11 | Journal Article |

On the Generation of Gaussian Noise

Saralees Nadarajah;Samuel Kotz

IEEE Transactions on Signal Processing

Year: 2007 | Volume: 55, Issue: 3 | Journal Article |

Cited by: Papers (7)

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The paper by Chapeau-Blondeau and Monir [IEEE Trans. Signal Process., vol.50, no.9, p.2160-5, 2002 September] addresses the problem of synthesizing a generalized Gaussian noise with exponent 1/2 by means of a nonlinear memoryless transformation applied to a uniform noise. The transformation involves the Lambert W function and the computation of this function required development of a rational appr...Show More

On the Generation of Gaussian Noise

Saralees Nadarajah;Samuel Kotz

IEEE Transactions on Signal Processing

Year: 2007 | Volume: 55, Issue: 3 | Journal Article |

Optimal inspection and perfect repair

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IMA Journal of Management Mathematics

Year: 2004 | Volume: 15, Issue: 2 | Journal Article |

Models are developed for decision making about monitoring and maintenance of systems whose performance through time is described by a general stochastic process. The system is monitored and preventive and corrective maintenance actions are carried out in response to the observed system state. The decision process is simplified by using the maximum process as a decision variable. The models develop...Show More

Optimal inspection and perfect repair

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IMA Journal of Management Mathematics

Year: 2004 | Volume: 15, Issue: 2 | Journal Article |

Discovering models of parallel workflow processes from incomplete event logs

Julijana Lekic;Dragan Milicev

2015 3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

Year: 2015 | Conference Paper |

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α-algorithm is able to discover a large class of workflow (WF) nets based on the behavior recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of business process models discovering based on examples of traces, i.e., logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modifi...Show More

Discovering models of parallel workflow processes from incomplete event logs

Julijana Lekic;Dragan Milicev

2015 3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

Year: 2015 | Conference Paper |

Signal Recovery for Incomplete Ocean Data via Graph Signal Processing

Zefeng Qi;Xuewen Liao;Ang Li;Chunlei Zheng

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

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In this paper, the signal recovery problem of incomplete ocean data based on graph signal processing is studied. In order to properly process the sparse and inhomogeneous data, the ocean data set is defined as a graph signal and its spatio-temporal characteristics is analyzed. The data are analyzed using the spatial smoothness of the signal based on topology, the correlation of time-varying signal...Show More

Signal Recovery for Incomplete Ocean Data via Graph Signal Processing

Zefeng Qi;Xuewen Liao;Ang Li;Chunlei Zheng

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Optimizing Skyline Query Processing in Incomplete Data

Yonis Gulzar;Ali A. Alwan;Sherzod Turaev

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (14)

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Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing)....Show More

Optimizing Skyline Query Processing in Incomplete Data

Yonis Gulzar;Ali A. Alwan;Sherzod Turaev

Year: 2019 | Volume: 7 | Journal Article |

Joint Signal Recovery and Graph Learning from Incomplete Time-Series

Amirhossein Javaheri;Arash Amini;Farokh Marvasti;Daniel P. Palomar

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics, which might not be available in some scenarios. In this work, we aim to learn a graph from incomplete time-series observations. From another viewpoint, we consider the problem of semi-blind recovery of time-varying grap...Show More

Joint Signal Recovery and Graph Learning from Incomplete Time-Series

Amirhossein Javaheri;Arash Amini;Farokh Marvasti;Daniel P. Palomar

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Construction of smart middleware system to support incomplete data based on WSN

Ryum-Duck Oh;Kwang-Won Lee;Joon-Hoon Park

2013 7th International Conference on Application of Information and Communication Technologies

Year: 2013 | Conference Paper |

Cited by: Papers (2)

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It currently is used for situation awareness service that is perceptible on what happens in the region where USN is installed. USN environment should maximize the energy efficiency. Sensing data from a very tiny sensor node and performing operations, and utilizing limited resources. This study designs and implements Active Smart Middleware, the USN Middleware which may possibly maximize data stabi...Show More

Construction of smart middleware system to support incomplete data based on WSN

Ryum-Duck Oh;Kwang-Won Lee;Joon-Hoon Park

2013 7th International Conference on Application of Information and Communication Technologies

Year: 2013 | Conference Paper |

A Joint Framework based on PU Learning and Active Learning for Object Detection

Yue Li;Minzheng Jia

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2023 | Conference Paper |

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The problem of object detection is a critical issue in the field of image processing, and the quality of labels can significantly affect the performance of detectors. In recent years, many deep learning frameworks have achieved good detection results. However, in many practical scenarios of object detection, collecting complete annotation is a time-consuming and costly process. Therefore, how to u...Show More

A Joint Framework based on PU Learning and Active Learning for Object Detection

Yue Li;Minzheng Jia

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

Year: 2023 | Conference Paper |

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