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Computational Social Networks 2019年第1期6卷 1-16页

作者： Pang, Yanxia Wang, Na Zhang, Ying Jin, Yuanyuan Ji, Wendi Tan, Wenan School of Computer Science and Software Engineering East China Normal University Shanghai China College of Engineering Shanghai Polytechnic University Shanghai China College of Information Liaoning University Liaoning China

Prerequisite inadequacy causes more MOOC drop-out. As an effective method interfering with learning process, existing MOOC recommendation is mainly about subsequent learning objects that have not been learned before. This paper proposes a locating-based MOOC recommendation method with consideration on prerequisite relationship. It locates the target learner’s learning paths and provides prerequisite recommendation for unqualified learning objects and subsequent recommendation for the qualified objects. Experiments on real-world data show both the accuracy and learning performance improvement of our recommendation method. © 2019, The Author(s).

关键词： Learning path MOOC Prerequisite Recommendation

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A Multi-Objective Natural Aggregation Approach Based Residential Energy Management with Photovoltaic Power Penetration

A Multi-Objective Natural Aggregation Approach Based Residen...

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Power and Electrical engineering (ACPEE), 5th Asia Conference on

作者： Zehua Zhao Fengji Luo Yanli Liu Ali Dorri Min Gao School of Big Data & Software Engineering Chongqing University Chongqing China School of Civil Engineering The University of Sydney Sydney Australia School of Electrical & Information Engineering Tianjin University Tianjin China School of Computer Science & Engineering The University of New South Wales Sydney Australia

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152820

Residential energy management (REM) has drawn wide attention in demand side management research recent years. This paper proposed a multi-objective optimization approach for REM in a high photovoltaic solar power penetrated environment. The proposed model manages three kinds of residential energy resources (RERs): a rooftop photovoltaic source, a heating, ventilation and air conditioning (HVAC) system, and multiple controllable household appliances. The proposed model aims at achieving following three objectives: (1) to minimize the home electricity cost; (2) to maximize the indoor thermal comfort for the user; and (3) to maximize the usage convenience of non-thermal appliances. An adaptive thermal comfort model is employed to model the indoor thermal comfort degree of the user, and a user disturbance metric is proposed to measure the psychological disturbance of the household appliance operation on the user's preference. A new multi-objective optimization solver, i.e., MultiObjective Natural Aggregation Algorithm (MONAA), is proposed to find the approximated Pareto frontier of the model. Simulations are conducted to validate the proposed method.

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Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

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Journal of computer science & Technology 2019年第5期34卷 1039-1062页

作者： Zhou Xu Shuai Pang Tao Zhang Xia-Pu Luo Jin Liu Yu-Tian Tang Xiao Yu Lei Xue College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science Wuhan UniversityWuhan 430072China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong 999077China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of Sciences Beijing 100190China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Science City University of Hong KongHong Kong 999077China

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.

关键词： cross-project defect prediction transfer learning balancing distribution effort-aware indicator

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Retraction Note: Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

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Neural Computing and Applications 2024年第19期36卷 11677-11677页

作者： Acharya, U. Rajendra Fujita, Hamido Sudarshan, Vidya K. Oh, Shu Lih Muhammad, Adam Koh, Joel E. W. Tan, Jen Hong Chua, Chua K. Chua, Kok Poo Tan, Ru San Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology SIM University Singapore Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Faculty of Software and Information Science Iwate Prefectural University (IPU) Iwate Japan Department of Cardiology National Heart Centre Singapore Singapore

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Stable Gaussian process tracking control of antagonistic variable stiffness actuators

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Scientific Reports 2025年第1期15卷 1-13页

作者： Zou, Zhigang Li, Wei Yang, Chenguang Pan, Yongping College of Mechatronics Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China School of Software Engineering Sun Yat-sen University Guangzhou 510006 China Department of Computer Science University of Liverpool Liverpool L69 3BX United Kingdom Peng Cheng Laboratory Shenzhen 518057 China School of Mathematical Sciences Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning 530006 China

The control of variable stiffness actuators (VSAs) is challenging because they have highly nonlinear characteristics and are difficult to model accurately. Classical control approaches using high control gains can make VSAs stiff, which alters the inherent compliance of VSAs. Iterative learning control can achieve high tracking accuracy for VSAs but generally lacks sufficient generalization. This study applies Gaussian process (GP) regression to design a stable tracking controller combining feedforward and low-gain feedback control actions for agonistic-antagonistic (AA) VSAs subjected to unknown dynamics. The GP model learns the inverse dynamics of AA-VSAs and provides model fidelity by the predicted variance. The stability analysis of the closed-loop system demonstrates that the tracking error is uniformly ultimately bounded and exponentially converges to a small ball under a given probability. Experiments on an AA-VSA named qbmove Advanced have validated the superiority of the proposed method with respect to tracking accuracy and generalization. © The Author(s) 2025.

关键词： Compliant actuation Gaussian process Machine learning for control Motion control Variable stiffness actuator

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Multispectral-Hyperspectral Image Fusion via Similarity-Guided Graph Attention and VAE-Transformer

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chi, Biwei Lu, Hangyuan Liu, Rixian Yang, Yong Xu, Lingrong Wan, Weiguo College of Information Engineering University of Vocational technology Jinhua China Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province Jinhua321007 China Tiangong University School of Computer Science and Technology Tianjin300387 China Cangzhou Institute of Tiangong University Cangzhou061000 China Jiangxi University of Finance and Economics School of Software and Internet of Things Engineering Nanchang330038 China

Fusion of a high-spatial-resolution multispectral image (MSI) and a low-spatial-resolution hyperspectral image (HSI) aims to generate a high-spatial-resolution HSI (HR-HSI). Most fusion methods use simple upsampling techniques to increase the resolution of HSI without guidance, which can introduce unwanted artifacts and lead to spectral distortion. Additionally, they face challenges in generalization and robustness. To address these challenges, this paper introduces a cross-modal fusion network for MSI and HSI, named CSGAV, which is built on similarity-guided graph attention (SGA) and a variational autoencoder-Transformer (VAET). Specifically, we develop a similarity measure algorithm to compute the similarity degree between the source images and construct an SGA module to mitigate modal differences, producing precise upsampled outputs. Moreover, we present an adaptive weighted Transformer, with the weights guided by a variational autoencoder, thereby enhancing the generalization and robustness of the model. The SGA and VAET are integrated in the cross-modal interactive architecture to achieve the final HR-HSI image. Experimental results conducted on four public datasets show that CSGAV is superior compared to existing state-of-the-art fusion methods both in fusion performance and generalization. © 1980-2012 IEEE.

关键词： Image coding

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Application of ontology in medical heterogeneous data integration 3

Application of ontology in medical heterogeneous data integr...

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3rd IEEE International Conference on Big Data Analysis, ICBDA 2018

作者： Ren, Shuxia Lu, Xu Wang, Teng School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (纸本)9781538647936

In the process of data integration, medical data is a huge obstacle to the development of medical information because of its complicated data type, large amount of data and semantic heterogeneity among different data sources. Integration of massive medical heterogeneous data is the urgent need to promote the process of medical information to be solved. According to the characteristics of medical data sources, an ontology-based medical heterogeneous data integration scheme is proposed. By introducing the medical similarity algorithm into the hybrid medical ontology, not only the multi-semantic problem is solved, but also the current heterogeneity of mass medical data is eliminated. Finally, experiments show that this method performs well in eliminating the semantic uncertainty of data in the medical field and can improve the integration efficiency of heterogeneous data in the medical data warehouse. It is practically feasible to solve the problem of medical heterogeneous data integration. © 2018 IEEE.

关键词： Data warehouses

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The Survey of CNN-based Cancer Diagnosis System 2

The Survey of CNN-based Cancer Diagnosis System

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2018 2nd Annual International Conference on Cloud Technology and Communication engineering, CTCE 2018

作者： Ruan, Mingzhe Ren, Yongshuo Wu, Zhiqi Wu, Jianrui Wang, Shuai Yang, Xulei School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

This thesis summarizes the structure and image processing methods of miniature robot from the perspectives of miniature diagnostic robot, CSF (CNN-SVM-FCN) and expert systems, in which it highlights the key technologies and the latest research progress in various aspects, analyzes the advantages and disadvantages of each method, elaborates their respective implementation processes and system framework. It also further explores ways to promote their complementary advantages and makes a prediction of the possible opportunities brought by the combination of these three methods. © Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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Dimensional modeling of medical data warehouse based on ontology 3

Dimensional modeling of medical data warehouse based on onto...

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3rd IEEE International Conference on Big Data Analysis, ICBDA 2018

作者： Ren, Shuxia Wang, Teng Lu, Xu School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (纸本)9781538647936

The two driving forces for designing dimensional models of data warehouse are data sources and business requirements. Medical data sources are characterized by too many data types, large amount of data, lack of associations among data, mixed dictionary tables, etc., which result in lacking uniform representation and effective organization among a large number of concepts. These features may lead to serious semantic heterogeneity among different data sources. Medical data warehouse design involves a wide range of business requirements, while the users' perspectives are different and the expression of semantic heterogeneity is substantial. So how to obtain and integrate these business requirements is a challenge. Aiming at the characteristics of medical data sources and business requirements, this paper proposes an ontology-based medical data warehouse dimensional modeling method. Experiment shows that this method can not only optimize the data warehouse requirements analysis process, but also effectively eliminate the semantic heterogeneity in data sources and business requirements, thereby greatly improving the efficiency of medical data warehouse dimensional modeling. © 2018 IEEE.

关键词： Semantics

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Li Chengbo Wang Xiaoming Huang Zixi Pang Guangyao Han Xinyu Lin Yaguang Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Middle School Shenzhen China School of Data Science and Software Engineering Wuzhou University Wuzhou China Advanced Robotics and Intelligent Control Centre Singapore Polytechnic Singapore

ISBN: (纸本)9781665406987

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the “Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm”. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.

关键词： Automation Heuristic algorithms Earthquakes Mathematical models Scheduling Optimization Rescue robots

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