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View-invariant human action recognition via robust locally adaptive multi-view learning

Jia-geng FENG,Jun XIAO

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 917-920 doi: 10.1631/FITEE.1500080

Abstract: In this paper, we present a multi-view learning approach to recognize human actions from different viewsAs most existing multi-view learning algorithms often suffer from the problem of lacking data adaptivenessin the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learningalgorithm based on learning multiple local L1-graphs is proposed.Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU,

Keywords： View-invariant     Action recognition     Multi-view learning     L1-norm     Local learning    

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Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning Article

Cong Wang, Shuaining Xie, Kang Li, Chongyang Wang, Xudong Liu, Liang Zhao, Tsung-Yuan Tsai

Engineering 2021, Volume 7, Issue 6,   Pages 881-888 doi: 10.1016/j.eng.2020.03.016

Abstract:

Deep-learning methods provide a promising approach for measuring in-vivoWe propose a feature-based transfer-learning method to extract features from fluoroscopic images.The proposed method provides a promising solution, using a learning-based registration method when only

Keywords： 2D–3D registration     Machine learning     Domain adaption     Point correspondence    

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Paper evolution graph: multi-view structural retrieval for academic literature None

Dan-ping LIAO, Yun-tao QIAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 187-205 doi: 10.1631/FITEE.1700105

Abstract:

Academic literature retrieval concerns about the selection of papers that are most likely to match a user’s information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph (PEG). The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.

Keywords： Paper evolution graph     Academic literature retrieval     Metagraph factorization     Topic coherence    

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Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingHowever, the common paradigm of the loss function in supervised learning requires large amounts of labeledTherefore, a fault detection method based on self-supervised feature learning was proposed to addressFirst, self-supervised learning was employed to extract features under various working conditions onlyThe self-supervised representation learning uses a sequence-based Triplet Loss.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Integrated energy view of wastewater treatment: A potential of electrochemical biodegradation

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 4, doi: 10.1007/s11783-021-1486-3

Abstract:

• Energy is needed to accelerate the biological wastewater treatment.

Keywords： Biological wastewater treatment     Integrated energy view     Electroactive bacteria     Extracellular electron    

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Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 311-315 doi: 10.1007/s11465-016-0376-z

Abstract: A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm

Keywords： multi-color space     k-nearest neighbor algorithm (k-NN)     self-learning     surge test    

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To Hold the Economical Time of Biology，to Practicea Positive View of Happiness

Gan Ziheng

Strategic Study of CAE 2007, Volume 9, Issue 2,   Pages 5-11

Abstract: definition, classification and characteristic of biological economy, also, the importance of positive viewIt also discusses which views of happiness should be recommended and how to practice a positive view

Keywords： biological economy     happiness     positive view of happiness    

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Robust object tracking with RGBD-based sparse learning Article

Zi-ang MA, Zhi-yu XIANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 989-1001 doi: 10.1631/FITEE.1601338

Abstract: In this paper, a novel RGBD and sparse learning based tracker is proposed.The range data is integrated into the sparse learning framework in three respects.First, an extra depth view is added to the color image based visual features as an independent view forthat the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning

Keywords： Object tracking     Sparse learning     Depth view     Occlusion templates     Occlusion detection    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structuralIn a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Max-margin basedBayesian classifier Article

Tao-cheng HU,Jin-hui YU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 973-981 doi: 10.1631/FITEE.1601078

Abstract: There is a tradeoff between generalization capability and computational overhead in multi-class learningWe propose a generative probabilistic multi-class classifier, considering both the generalization capabilityand the learning/prediction rate.By convex and probabilistic analysis, an efficient online learning algorithm is developed.

Keywords： Multi-class learning     Max-margin learning     Online algorithm    

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Understand the local and regional contributions on air pollution from the view of human health impacts

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 5, doi: 10.1007/s11783-020-1382-2

Abstract:

• PM2.5-related deaths were estimated to be 227 thousand in BTH & surrounding regions.

Keywords： PM2.5     Regional transport     Local emissions     Health impact     Environmental inequality    

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River restoration challenges with a specific view on hydromorphology

Jianhua LI, Stephan HOERBINGER, Clemens WEISSTEINER, Lingmin PENG, Hans Peter RAUCH

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1033-1038 doi: 10.1007/s11709-020-0665-9

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords： water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution    

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Sliding window games for cooperative building temperature control using a distributed learning method

Zhaohui ZHANG, Ruilong DENG, Tao YUAN, S. Joe QIN

Frontiers of Engineering Management 2017, Volume 4, Issue 3,   Pages 304-314 doi: 10.15302/J-FEM-2017045

Abstract: During the games, a distributed learning algorithm based on game theory is proposed such that each building

Keywords： game theory     demand response     HVAC control     multi-building system    

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Title Author Date Type Operation

View-invariant human action recognition via robust locally adaptive multi-view learning

Jia-geng FENG,Jun XIAO

Journal Article

Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning

Cong Wang, Shuaining Xie, Kang Li, Chongyang Wang, Xudong Liu, Liang Zhao, Tsung-Yuan Tsai

Journal Article

Paper evolution graph: multi-view structural retrieval for academic literature

Dan-ping LIAO, Yun-tao QIAN

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

Integrated energy view of wastewater treatment: A potential of electrochemical biodegradation

Journal Article

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Journal Article

To Hold the Economical Time of Biology，to Practicea Positive View of Happiness

Gan Ziheng

Journal Article

Robust object tracking with RGBD-based sparse learning

Zi-ang MA, Zhi-yu XIANG

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Max-margin basedBayesian classifier

Tao-cheng HU,Jin-hui YU

Journal Article

Understand the local and regional contributions on air pollution from the view of human health impacts

Journal Article

River restoration challenges with a specific view on hydromorphology

Jianhua LI, Stephan HOERBINGER, Clemens WEISSTEINER, Lingmin PENG, Hans Peter RAUCH

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Sliding window games for cooperative building temperature control using a distributed learning method

Zhaohui ZHANG, Ruilong DENG, Tao YUAN, S. Joe QIN

Journal Article