The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 r...
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ISBN:
(数字)9783642208478
ISBN:
(纸本)9783642208461
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, machine learning, artificial intelligence and pattern recognition, data warehousing and databases, statistics, knowledge engineering, behavior sciences, visualization, and emerging areas such as social network analysis.
The two-volume set LNCS 7565 and 7566 constitutes the refereed proceedings of three confederated international conferences: Cooperative informationsystems (CoopIS 2012), Distributed Objects and Applications - Secure ...
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ISBN:
(数字)9783642336065
ISBN:
(纸本)9783642336058
The two-volume set LNCS 7565 and 7566 constitutes the refereed proceedings of three confederated international conferences: Cooperative informationsystems (CoopIS 2012), Distributed Objects and Applications - Secure Virtual Infrastructures (DOA-SVI 2012), and Ontologies, DataBases and Applications of SEmantics (ODBASE 2012) held as part of OTM 2012 in September 2012 in Rome, Italy. The 53 revised full papers presented were carefully reviewed and selected from a total of 169 submissions. The 22 full papers included in the first volume constitute the proceedings of CoopIS 2012 and are organized in topical sections on business process design; process verification and analysis; service-oriented architectures and cloud; security, risk, and prediction; discovery and detection; collaboration; and 5 short papers.
The variable preconditioned (VP) Krylov subspace method with mixed precision is implemented on graphics processing unit (GPU) using compute unified device architecture (CUDA), and the linear system obtained from the e...
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Design of complex artifacts and systems requires the cooperation of multidisciplinary design teams using multiple sophisticated commercial and non-commercial engine- ing tools such as CAD tools, modeling, simulation a...
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ISBN:
(数字)9783540927198
ISBN:
(纸本)9783540927181
Design of complex artifacts and systems requires the cooperation of multidisciplinary design teams using multiple sophisticated commercial and non-commercial engine- ing tools such as CAD tools, modeling, simulation and optimization software, en- neering databases, and knowledge-based systems. Individuals or individual groups of multidisciplinary design teams usually work in parallel and independently with various engineering tools, which are located on different sites, often for quite a long period of time. At any moment, individual members may be working on different versions of a design or viewing the design from various perspectives, at different levels of details. In order to meet these requirements, it is necessary to have efficient comput- supported collaborative design systems. These systems should not only automate in- vidual tasks, in the manner of traditional computer-aided engineering tools, but also enable individual members to share information, collaborate, and coordinate their activities within the context of a design project. Based on close international collaboration between the University of technology of Compiègne in France and the Institute of Computing technology of the Chinese Ac- emy of Sciences in the early 1990s, a series of international workshops on CSCW in Design started in 1996. In order to facilitate the organization of these workshops, an International Working Group on CSCW in Design (CSCWD) was established and an International Steering Committee was formed in 1998. The series was converted to int- national conferences in 2000 building on the success of the four previous workshops.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
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