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Application of Two-Dimensional Predictive Functional Control in Injection Molding

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2015年第41期54卷 10088-10102页

作者： Yang, Bo Xu, Zuhua Yang, Yi Gao, Furong Zhejiang Univ Coll Control Sci & Engn Natl Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Kunda Mold Shen Zhen Co Ltd Fuda Intelligent Syst Shenzhen 518129 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Hong Kong Hong Kong Peoples R China

A batch process can be viewed as a two-dimensional (2D) system with dynamics in both time and batch directions. To tackle these 2D dynamics, many control algorithms have been proposed by combining iterative learning control (TLC) with high-performance continuous control algorithms under a 2D framework. However, these algorithms require large computational load and memory, which are often demanding for the software and hardware of the controllers in several fast batch processes. In this paper, a 2D predictive functional control algorithm that combines ILC with predictive functional control (PFC) is proposed. Owing to the compactness and effectiveness of PFC, the proposed control algorithm can reduce the required computational load and memory. Other than the general form of the control law, two concise and practical forms are provided as well. The proposed control scheme is tested through simulations and implementation in an injection molding process, which is a typical fast batch process. Results confirm the good performance of the proposed control algorithm.

关键词： Batch processing predictive functional control Injection molding international Labour conference Controllers compactness Power factor correction computer algorithms

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Sparse learning for robust background subtraction of video sequences 11th

Sparse learning for robust background subtraction of video s...

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11th international conference on Intelligent Computing, ICIC 2015

作者： Luo, Yuhan Zhang, Hong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9783319221793

Sparse representation has been applied to background detecting by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based methods only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the foreground and the background. Learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. To take these challenges, this paper proposes a new method for robust background detecting via sparse representation. Our method explores both the strength of the well-patch adaptive dictionary learning technique to video frame structure analysis and the robustness background detection by the l1-norm data-fidelity term. By using linear sparse combinations of dictionary atom, the proposed method learns the sparse representations of video frame regions corresponding to candidate particles. The experiments show that the proposed method is able to tolerate the background clutter and video frame deterioration, and improves the existing detecting performance. © Springer international Publishing Switzerland 2015.

关键词： Image representation

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How to Find Appropriate Automobile Exhibition Halls: Towards a Personalized Recommendation service for Auto Show 15

How to Find Appropriate Automobile Exhibition Halls: Towards...

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IEEE 15th international conference on Data Mining Workshops (ICDMW)

作者： Guo, Danhuai Zhu, Yingqiu Xu, Wei Ge, Yong Zhou, Yuanchun Li, Jianhui Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ China Sch Informat Renming Beijing Peoples R China UNC Charlotte Coll Comp & Informat Charlotte NC USA

ISBN: (纸本)9781467384933

This paper proposes a novel recommendation service to help visitors to find their proper automobile exhibition halls for auto show. In the proposed method, both temporal and spatial features of visitors are first considered to construct their profiling, and then extract their interests based on visitors' clustering. Finally, highly desired exhibition halls are personalized recommended to proper visitor. The proposed recommender system consists of three modules including relevance module, quality module and integration module. The relevance module is developed to measure the relationship of an automobile exhibition and a visitor, while the quality module is constructed to analyze the quality of each automobile exhibition. The integration module is to combine two modules above for appropriate automobile exhibition. The proposed approach is well validated using a real world dataset, and compared with several baseline models. Our experimental results indicate that in terms of the well-known evaluation metrics, the proposed method can achieves more useful and feasible recommendation results, and our finding highlights that the proposed method can help both visitors to find a more appropriate automobile exhibition halls, and manage officers to reduce more management cost.

关键词： Auto show automobile exhibition halls recommendation profiling

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Study of test classification algorithm based on domain knowledge 3

Study of test classification algorithm based on domain knowl...

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3rd international conference on Cyberspace Technology, CCT 2015

作者： Gao, Yutong Song, Feifan Xie, Xiaqing Sun, Qing Wu, Xu Department of Computer Science and Information Engineering National Taipei University of Technology Taibei China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Ministry of Education Beijing China Beijing University of Posts and Telecommunications Library Beijing China

ISBN: (纸本)9781785610899

When facing various and massive data resources, how to effectively utilize the resources according to the division field is one of the core problem of the institutional repository research. In this paper, we improved Bayesian classification algorithm, then proposed a text classification algorithm based on domain knowledge. Furthermore, some key technologies such as text classification, feature selection, weight improvement and domain knowledge algorithm improvement are designed and implemented. We use widely applied IkAnalyzer method to classify Chinese words. For feature selection and weight improvement part, we focus on the processing of special vocabulary in the document. We introduce the field expand vocabulary assist the Bayesian formula in the field application part to obtain the final result. The experiment result shows that the improved algorithm enhanced the accuracy of the classification efficiently, and the system calculating time is acceptable.

关键词： Domain Knowledge

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Ensuring Data Security and Performance Evaluation in Cloud Computing

Ensuring Data Security and Performance Evaluation in Cloud C...

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1st international conference on Intelligent Computing, Communication and Devices, ICCD 2014

作者： Shamsolmoali, Pourya Afshar Alam, M. Department of Computer Science Jamia Hamdard University New Delhi India

ISBN: (纸本)9788132220114

Cloud computing is threatened by unanswered security issues that are risky for both the cloud providers and users. Cloud is a computing design that manages large sets of distributed resources, of which scientists benefit from their convergence. The aim of this paper is divided into two parts: firstly a brief review on cloud computing mainly focusing on security and secondly offer a solution that eradicates possible threats. In particular, we proposed a new data security model that can efficiently protect the data whether in the cloud database or in transition. We start with an established authentication server and data server providing user authentication, user verification, and data support. The system follows SSL protocol for data encryption and protection, and secure deliver report (SDR) is used for data reliability and integrity of communication. © Springer India 2015.

关键词： Cryptography

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Parallel bloom filter on xeon phi many-core processors 15th

Parallel bloom filter on xeon phi many-core processors

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15th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Ni, Sheng Guo, Rentong Liao, Xiaofei Jin, Hai Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319271217

Bloom filters are widely used in databases and network areas. These filters facilitate efficient membership checking with a low false positive ratio. It is a way to improve the throughput of bloom filter by parallel processing. Common many-core processors such as Xeon Phi can provide high parallelism. Thus, we build an iterative model to analyze memory access performance. This performance suggests that the bottleneck in the traditional design is mainly caused by synchronization cost and memory latency on many-core platforms. Therefore, we propose a parallel bloom filter (PBF), which is a lockless method involving input data preprocessing. This method reduces synchronization overhead and improves cache locality. We also implement and evaluate PBF on a Xeon Phi processor. Results show that the memory access performance is three times better than that of the counting bloom filter. PBF provides improved scalability, and the speedup ratio can reach a maximum of 80.7x. © Springer international Publishing Switzerland 2015.

关键词： Scalability

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Supporting rule generation and validation on environmental data in EnStreaM 1

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Extended Semantic Web conference, ESWC 2012

作者： Moraru, Alexandra Kenda, Klemen Fortuna, Blaž Bradeško, Luka Škrjanc, Maja Mladenić, Dunja Fortuna, Carolina Artificial Intelligence Laboratory Jožef Stefan Institute Ljubljana Slovenia Department of Communication Systems Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia

ISBN: (数字)9783662466414

ISBN: (纸本)9783662466407

Detection rules represent one of the components of the rule models in event processing systems. These rules can be discovered from data using data mining techniques or domain experts’ knowledge. We demonstrate a system that provides its users the means for creating and validating such rules. The system is applied on real-life environmental scenarios, where the main source of data comes from sensors. Based on historical data about events of interest, the scope is to formulate rules that could have caused these events. Using a scalable infrastructure the rules can be tested on massive amount of data in order to observe how past events would fit to these rules. In addition, we create semantic annotations of the dataset and use them in the system outputs in order to support interoperability with other systems. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Interoperability

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Unified virtual memory support for deep CNN accelerator on soC FPGA 1

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15th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Xiao, Tao Qiao, Yuran Shen, Junzhong Yang, Qianming Wen, Mei College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

Cooperation of CPU and hardware accelerator on SoC FPGA to accomplish computational intensive tasks, provides significant advantages in performance and energy efficiency. However, current operating systems provide little support for accelerators: the OS is unaware that a computational task can be executed either on a CPU core or an accelerator, and provides no assistance in efficient management of data sharing between CPU and accelerator on the DRAM, such as zero copy, data coherence. It’s also hard for current OS to allocate large contiguous physical memory space for accelerator. In this paper, we select the Xilinx ZYNQ as target and qualitatively analyze methods of sharing data. Besides using high-performance (HP) AXI interfaces of the ZYQN device, we develop a novel memory management system for FPGA-based accelerator. It provides a unified virtual space for CPU cores and accelerator so that they can access the same memory space in the operating systems user space. For a deep convolutional neural network task, our design gains up to speed-up of 5. 34x compared to traditional processoraccelerator cooperation. © Springer international Publishing Switzerland 2015.

关键词： Energy efficiency

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Designing states, actions, and rewards for using POMDP in session search 37

Designing states, actions, and rewards for using POMDP in se...

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37th European conference on Information Retrieval Research, ECIR 2015

作者： Luo, Jiyun Zhang, Sicong Dong, Xuchu Yang, Hui Department of Computer Science Georgetown University 37th and O Street NW WashingtonDC20057 United States

ISBN: (纸本)9783319163536

Session search is an information retrieval task that involves a sequence of queries for a complex information need. It is characterized by rich user-system interactions and temporal dependency between queries and between consecutive user behaviors. Recent efforts have been made in modeling session search using the Partially Observable Markov Decision Process (POMDP). To best utilize the POMDP model, it is crucial to find suitable definitions for its fundamental elements – States, Actions and Rewards. This paper investigates the best ways to design the states, actions, and rewards within a POMDP framework. We lay out available design options of these major components based on a variety of related work and experiment on combinations of these options over the TREC 2012 & 2013 Session datasets. We report our findings based on two evaluation aspects, retrieval accuracy and efficiency, and recommend practical design choices for using POMDP in session search. © Springer international Publishing Switzerland 2015.

关键词： Behavioral research

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On exploring a virtual agent negotiation inspired approach for route guidance in urban traffic networks 15th

On exploring a virtual agent negotiation inspired approach f...

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15th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Hu, Wenbin Yan, Liping Wang, Huan Du, Bo School of Computer Wuhan University Wuhan430072 China

ISBN: (纸本)9783319271361

The traditional route guidance system often provides the same shortest route to different drivers regardless of their different traffic conditions. As a result, many vehicles may rush into the same road segments at the same time that would lead to traffic congestion. Such uncontrolled dispersion of vehicles can be avoided by evenly distributing vehicles along the potential routes. This paper proposes a practical Virtual Agent Negotiation based Route Guidance Approach (VANRGA). In the proposed approach, vehicle agents (VAs) in the local vicinity communicate with each other before the intersections to achieve a real-time and dynamic route selection. Based on the route preference of the drivers and the traffic conditions, the vehicles are distributed on the routes equally, which can avoid the traffic congestion and maximize the utility of the road resources. After presenting the design and implementation methodology of VANRGA, this paper carries out extensive experiments on synthetic and real-world road networks. The experimental results show that compared to the shortest path algorithms, VANRGA offers a 22%-37% decrease in travel time (when traffic demand is below network capacity) and a 15%-18% decrease in travel time (when traffic demand exceeds network capacity). © Springer international Publishing Switzerland 2015.

关键词： Vehicles

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