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Error Modeling and Anomaly Detection of Smart Electricity Meter Using TSVD+L Method Journal article
Lidan Chen, Keng-Weng Lao, Yongliang Ma, Zhe Zhang. Error Modeling and Anomaly Detection of Smart Electricity Meter Using TSVD+L Method[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71, 3521314.
Authors:  Lidan Chen;  Keng-Weng Lao;  Yongliang Ma;  Zhe Zhang
Favorite | TC[WOS]:5 TC[Scopus]:14  IF:5.6/5.6 | Submit date：2022/08/26
Anomaly Detection (Ad)  L-curve Optimization  Sliding Window Technique (Swt)  Smart Electricity Meter (Sm)  Truncated Singular Value Decomposition (Tsvd) Regularization  
Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns Journal article
Lei, Iat Long, Teh, Phoey Lee, Si, Yain Whar. Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns[J]. APPLIED SOFT COMPUTING, 2021, 107, 107363.
Authors:  Lei, Iat Long;  Teh, Phoey Lee;  Si, Yain Whar
Favorite | TC[WOS]:4 TC[Scopus]:6  IF:7.2/7.0 | Submit date：2021/12/08
Chart Patterns  Direct Least Squares Fitting Of Ellipses  Financial Time Series  Segmentation  Sliding Window  
Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wan, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study[J]. Neurophotonics, 2019, 6(2), 025010.
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wan;  Xuanyu Li;  Zhen Yuan; et al.
Adobe PDF | Favorite | TC[WOS]:32 TC[Scopus]:32 | Submit date：2022/08/21
CommunicAtion WithIn The BraIn Is Highly Dynamic. AlzheI.e.’s dIseAse (Ad) ExhibIts Dynamic.progression COrrespondIng To a DeclIne In MemOry And Cognition. HoWever, Little Is Known Of wheTher BraIn Dynamic. Are dIsrupted In Ad And Its Prodromal Stage, Mild CognitI.e.impairment (Mci). FOr Our Study, We AcquI.e. High samplIng RAte Functional near-InfrAred Spectroscopy imagIng DAta At Rest From The EntI.e.cOrtex Of 23 pAtients With Ad Dementia, 25 pAtients With Amnestic Mild CognitI.e.impairment (aMci), And 30 age-mAtched Healthy Controls (Hcs). slidIng-wIndow cOrrelAtion And K-means clusterIng Analyses Were Used To Construct Dynamic.Functional Connectivity (Fc) Maps FOr Each Participant. We dIscovered thAt The BraIn’s Dynamic.Fc Variability Strength (q) Significantly IncreAsed In Both aMci And Ad Group As compAred To Hcs. usIng The q Value As a meAsurement, The clAssificAtion perFOrmance ExhibI.e. a Good poWer In differentiAtIng aMci [Area Under The Curve (Auc ¼ 82.5%)] Or Ad (Auc ¼ 86.4%) From Hcs. furThermOre, We Identified Two abnOrmal BraIn Fc stAtes In The Ad Group, Of Which The Occurrence Frequency (f) ExhibI.e. a Significant decreAse FOr The First-level Fc stAte (stAte 1) And a Significant IncreAse FOr The Second-level Fc stAte (stAte 2). We Also Found thAt The abnOrmal f In These Two stAtes Significantly cOrrelAted With The CognitI.e.impairment In pAtients. These fIndIngs provI.e.The First EvI.e.ce To demonstRAte The dIsruptions Of Dynamic.BraIn Connectivity In aMci And Ad And Extend The trAditional stAtic (I.e., tI.e.averaged) Fc fIndIngs In The dIseAse (I.e., dIsconnection Syndrome) And Thus provI.e.Insights InTo UnderstAndIng The pAthophysiological mechanIsms occurrIng In aMci And Ad.  
Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wang, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study[J]. NEUROPHOTONICS, 2019, 6(2).
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wang;  Xuanyu Li;  Zhen Yuan; et al.
Favorite | TC[WOS]:32 TC[Scopus]:32  IF:4.8/4.3 | Submit date：2020/06/24
Resting State  Functional Connectivity  Sliding-window  K-means Clustering  Brain Network  Alzheimer's Disease  
A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis Journal article
Lina Yang, Yuan Yan Tang, Yang Lu, Huiwu Luo. A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(2), 348-359.
Authors:  Lina Yang;  Yuan Yan Tang;  Yang Lu;  Huiwu Luo
Favorite | TC[WOS]:15 TC[Scopus]:20  IF:3.6/3.2 | Submit date：2019/02/11
Discrete Wavelet Transform  Fractal Dimension  Higuchi's Algorithm  Protein Sequence Similarity  Sliding Window  
Optimized Trajectory Recovery of On-Road Vehicles from MonocularVideos with Multiple Constraints Journal article
Zheng, Yali, Cheng, Hong, Tang, Yuan Yan, Fang, Bin. Optimized Trajectory Recovery of On-Road Vehicles from MonocularVideos with Multiple Constraints[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2014, 9(2), 200-206.
Authors:  Zheng, Yali;  Cheng, Hong;  Tang, Yuan Yan;  Fang, Bin
Favorite | TC[WOS]:1 TC[Scopus]:0  IF:1.0/0.9 | Submit date：2018/11/06
Structure From Motion  Trajectory Recovery  Sliding Window  Optimal  
Noise reduction algorithms using fibonacci fourier transforms Conference paper
Agaian S.S., Chen M.-C., Chen C.L.P.. Noise reduction algorithms using fibonacci fourier transforms[C], 2008, 1048-1052.
Authors:  Agaian S.S.;  Chen M.-C.;  Chen C.L.P.
Favorite | TC[WOS]:0 TC[Scopus]:4 | Submit date：2019/02/11
Fast Fibonacci Fourier Transforms  Fibonacci Fourier Transforms  Noise Reduction  Sliding Window Filters  
Fibonacci Fourier transform and sliding window filtering Conference paper
Agaian S.S., Chen C.L.P., Chen M.-C.. Fibonacci Fourier transform and sliding window filtering[C], 2007.
Authors:  Agaian S.S.;  Chen C.L.P.;  Chen M.-C.
Favorite | TC[WOS]:0 TC[Scopus]:3 | Submit date：2019/02/11
Fast Fibonacci Fourier Transform  Fibonacci Fourier Transform  Fusion Sliding Window Filtering  Image De-noising  Sliding Double Window Filtering  Threshold Filtering