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A Hybrid Data-Driven Approach for Predicting Remaining Useful Life of Industrial Equipment 1

6th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Tan, Zheng Wen, Yiping Li, TianCai School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9789811579844

ISBN: (纸本)9789811579837

Guaranteeing the safety of equipment is extremely important in industry. To improve reliability and availability of equipment, various methods for prognostics and health management (PHM) have been proposed. Predicting remaining useful life (RUL) of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues. With the rapid development of industrial equipment and sensing technology, an increasing amount of data on the health level of equipment can be obtained for RUL prediction. This paper proposes a hybrid data-driven approach based on stacked denoising autoencode (SDAE) and similarity theory for estimating remaining useful life of industrial equipment, which is named RULESS. Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction. The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation (C-MAPSS). © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Stacking Ensemble Learning-based Gender Identification for User Profiling in Smart Education

Stacking Ensemble Learning-based Gender Identification for U...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Qiang Fu Yiping Wen Zheng Tan Qi Fu School of Computer Science and Engineering Hunan University of Science & Technology Xiang Tan China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology XiangTan China

ISBN: (纸本)9781665436885

To improve company profit, user profiling has become an important research topic in understanding customers and improving products or services, in which identifying users' gender is a fundamental problem. However, little research work was carried out on user profiling for improving learning in the domain of Smart Education. By analyzing Chinese microblog data, this paper proposes a stacking ensemble learning-based gender identification method for user profiling. It extracts gender features according to user published microblog messages and uses the base learner to extract stacking features, which are used as input to the meta-learner to obtain gender identification results. Its effectiveness is verified by comparison experiments with existing algorithms such as Logistic Regression and Random Forest for social media user gender identification.

关键词： Social networking (online) Stacking Blogs Education Text categorization Companies Feature extraction

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Web Service Classification Based on Reinforcement Learning and Structured Representation Learning

Web Service Classification Based on Reinforcement Learning a...

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Artificial Intelligence and Blockchain Technology (AIBT), International Conference on

作者： Hankang Sheng Zhangbing Li Jianxun Liu Xiao Zhang School of Computer Science and Engineering Hunan University of Science & Technology Xiang Tan China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology Xiang Tan China

ISBN: (纸本)9781665432689

In recent years, the rapid growth of the number of web services on the Internet has made it difficult to discover services from a large number of web services and classify them efficiently, which poses a huge challenge for users to provide appropriate web services. The existing web service classification technology has problems such as sparse description text, insufficient consideration of attribute information and structural relationships, so the accuracy of web service classification can be further improved. In response to the above problems, this paper first proposes an information distillation model based on reinforcement learning. Reinforcement learning is introduced to delete redundant information and retain valid information. Then, the classification network classifies the distilled information and determines the reward based on the prediction result. Then, the reward is fed to reinforcement learning for gradient update and the classification network is updated inversely according to the classification result. It can effectively alleviate the inherent noise caused by the introduction of the attention mechanism. It can also learn classification tasks by identifying important words or task-related structures without displaying structural annotations (ie, artificially building models). Through comparative experiments on the real data set of Programmable Web, the results show that the proposed model achieves better results on the benchmark data set than other methods.

关键词： Representation learning Web services Buildings Reinforcement learning Benchmark testing Data models Blockchains

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An Entropy-Based Model for Recommendation of Taxis’Cruising Route

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Journal on Artificial Intelligence 2020年第3期2卷 137-148页

作者： Yizhi Liu Xuesong Wang Jianxun Liu Zhuhua Liao Yijiang Zhao Jianjun Wang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201China

Cruising route recommendation based on trajectory mining can improve taxi-drivers'income and reduce energy ***,existing methods mostly recommend pick-up points for taxis ***,their performance is not good enough since there lacks a good evaluation model for the pick-up ***,we propose an entropy-based model for recommendation of taxis'cruising ***,we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal ***,the information entropy of spatial-temporal features is integrated in the evaluation *** it is applied for getting the next pick-up points and further recommending a series of successive *** points are constructed a cruising route for *** results show that our method is able to obviously improve the recommendation accuracy of pick-up points,and help taxi-drivers make profitable benefits more than before.

关键词： Trajectory data mining location-based services(LBS) optimal route recommendation pick-up point recommendation information entropy

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A Hybrid Data-Driven Approach for Predicting Remaining Useful Life of Industrial Equipment

A Hybrid Data-Driven Approach for Predicting Remaining Usefu...

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2020国际计算机前沿大会

作者： Zheng Tan Yiping Wen TianCai Li School of Computer Science and Engineering Hunan University of Science and Technology Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology

Guaranteeing the safety of equipment is extremely important in industry. To improve reliability and availability of equipment, various methods for prognostics and health management(PHM) have been proposed. Predicting remaining useful life(RUL) of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues. With the rapid development of industrial equipment and sensing technology, an increasing amount of data on the health level of equipment can be obtained for RUL prediction. This paper proposes a hybrid data-driven approach based on stacked denoising autoencode(SDAE) and similarity theory for estimating remaining useful life of industrial equipment, which is named RULESS. Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction. The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation(C-MAPSS).

关键词： Remainingusefullife Prediction Industrialequipment StackedDenoisingAutoEncoder Similaritytheory RULESS

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Edge Intelligence Empowered Distribution Path Planning with Internet of Vehicles

Edge Intelligence Empowered Distribution Path Planning with ...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tiancai Li Yiping Wen Zheng Tan Hong Chen School of Computer Science and Engineering Hunan University of Science and Technology China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science and Technology

ISBN: (纸本)9781728176505

With rapid urbanization and increasing traffic, distribution path planning has become an important challenging issue. Traditional vehicle distribution route planning mostly relies on static road information or historical traffic data for route planning. However, route planning based on such information may be unreasonable, because many uncertainty factors involve in the vehicle distribution process. Edge intelligence can help to handle such uncertainty factors, as it can offer smart services in close proximity to the Internet of Vehicles (IoV) environment with low latency and less cost. Therefore, in this paper, a model for dynamic distribution path planning with IoV and edge intelligence is constructed, and a corresponding planning strategy is proposed. The result of simulation experiments shows its effectiveness.

关键词： Uncertainty Intelligent vehicles Computational modeling Roads High performance computing Path planning Planning

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A Campus Carpooling System Based on GPS Trajectories 1

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12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Wang, Xuesong Liu, Yizhi Jiang, Zhengtao Peng, You Yin, Tianhao Liao, Zhuhua Zhao, Jingqiang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201 China Network Information Center Hunan University of Science and Technology Xiangtan411201 China

ISBN: (数字)9783030249007

ISBN: (纸本)9783030248994

College students are special because they relatively have tighter in economy but have greater consistency in leisure time. They prefer to go out together with schoolfellows due to higher trusts and closeness. Moreover, the electronic map is difficult to be updated. Campus-roads recently are updated rapidly. And many alleys in campuses are not shown in the electronic map. Therefore, we devise and implement a campus carpooling system based on GPS trajectories. It includes three parts. Firstly, the campus road network is extracted based on GPS trajectories. Next, the shortest sharing path in the campus is computed in terms of the campus road network. Then, passengers are matched automatically by the carpooling matching algorithm (CMA) in our system. Experiments show that our system is able to provide a safer and more comfortable carpooling experience for college students. © 2019, Springer Nature Switzerland AG.

关键词： Trajectories

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A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Scheduling algorithms

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Learning resource recommendation based on Generalized Matrix Factorization and Long Short-Term Memory model

Learning resource recommendation based on Generalized Matrix...

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IEEE International Conference on Cloud Computing Technology and Science

作者： Tianhang Guo Yiping Wen Feiran Wang Junjie Hou School of Computer Science and Engineering Hunan University of Science & Technology China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China

Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.

关键词：

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Towards Energy-Efficient Scheduling with Batch processing for Instance-Intensive Cloud Workflows

Towards Energy-Efficient Scheduling with Batch Processing fo...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhibin Wang Yiping Wen Jinjun Chen Buqing Cao Feiran Wang Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China School of Computer Science and Engineering Hunan University of Science & Technology China

ISBN: (纸本)9781728111421;9781728111414

Instance-intensive workflow applications are widely used in the areas of electronic commerce, advanced manufacture, etc. However, existing studies normally do not consider the problem of reducing their energy consumption by utilizing the advantages of batch processing strategy and the characters of instance-intensive workflow applications. This paper presents an Energy-efficient Instance-intensive Cloud workflows scheduling method with Batch processing, named EICB. Technically, the method is promoted with strategies to merge several activity instances and balance resource utilization of physical machines (PMs) to improve energy efficiency for instance-intensive cloud workflows. The effectiveness and efficiency of the proposed method are validated by extensive experiments.

关键词： Batch production systems Energy consumption Cloud computing Resource management Energy efficiency Job shop scheduling Monitoring

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