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FRAIPA: A fast recommendation approach with improved prediction accuracy

EXPERT SYSTEMS WITH APPLICATIONS 2017年 87卷 90-97页

作者： Hammou, Badr Ait Lahcen, Ayoub Ait Mohammed V Univ Fac Sci Associated Unit CNRST URAC 29 LRIT Rabat Morocco Ibn Tofail Univ Natl Sch Appl Sci ENSA LGS Kenitra Morocco

Recommender systems have emerged as a key tool to overcome the negative impact of information overload problem, as well as, help the users seek the relevant information based on their past preferences. Collaborative filtering represents a widely used approach to build recommendation systems. In essence, many methods have been developed to provide high quality results, neverthless, they may incur prohibitive computational costs. In this paper, a novel method called FRAIPA is proposed, which is designed to tackle the sparsity, dynamic data problems, moreover, it improves the prediction accuracy and computation time. Experimental results on two real-world data sets reveal the effectiveness of the proposed method over existing methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Recommender system Rating prediction computation time Collaborative filtering

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Event-Triggered State Estimation: Experimental Performance Assessment and Comparative Study

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2017年第5期25卷 1865-1872页

作者： Chen, Wentao Shi, Dawei Wang, Junzheng Shi, Ling Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China

In this brief, an experimental and comparative study of event-based state estimation is performed for some typical event-triggering conditions. The experiments are performed on a magnetic-brake loaded permanent-magnet dc torque motor system. The comparisons are performed from three aspects: 1) performance under different average sensor-to-estimator communication rates;2) effect of inaccurate estimates of the noise covariance matrices;and 3) computation complexity. The comparative results show that the innovation-based schedules are relatively superior to the "send-on-delta" schedules in terms of estimation quality, and the deterministic schedules have enhanced estimation performance compared with stochastic counterparts. The estimators considered are sensitive to the estimates of the noise covariance matrices, and retain a similar level of computation complexity as that of the standard Kalman filter.

关键词： computation time event-based state estimation event-triggered transmission motor control system robustness

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Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic-probabilistic power flow based on RBFNNs

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IET RENEWABLE POWER GENERATION 2017年第6期11卷 867-877页

作者： Baghaee, Hamid Reza Mirsalim, Mojtaba Gharehpetian, Gevork B. Talebi, Heidar Ali Amirkabir Univ Technol Elect Engn Dept Tehran Iran

The probabilistic power flow (PPF) of active distribution networks and microgrids based on the conventional power flow algorithms is almost impossible or at least cumbersome. Always, Mont Carlo simulation is a reliable solution. However, its computation time is relatively high that makes it unattractive to be a reliable solution for large interconnected power systems. This study presents a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) for possibilistic-PPF in the microgrids including uncertain loads, correlated wind and solar distributed energy resources and plug-in hybrid electric vehicles. When sufficient historical data of the system variables is not available, a probability density function might not be defined, while they must be represented in another way namely possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. The proposed method exploits the ability of RBFNN and unscented transform in non-linear mapping with an acceptable level of accuracy, robustness and reliability. Simulation results for the proposed PPF algorithm and its comparison with the reported methods for different test power systems reveals its efficiency, accuracy, robustness and authenticity.

关键词： distributed power generation transforms fuzzy set theory load flow Monte Carlo methods computational complexity radial basis function networks hybrid electric vehicles power system interconnection distribution networks wind power power engineering computing test power systems PPF algorithm nonlinear mapping historical data plug-in hybrid electric vehicles solar distributed energy resources uncertain loads radial basis function neural networks fuzzy unscented transform large interconnected power systems computation time Monte Carlo simulation conventional power flow algorithms active distribution networks RBFNN possibilistic power flow probabilistic power flow PV microgrids correlated wind microgrids uncertainty quantification

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Motion depth generation using MHI for 2D-to-3D video conversion

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ELECTRONICS LETTERS 2017年第23期53卷 1520-1521页

作者： Gil, J. Kim, M. Kangwon Natl Univ Dept Comp Engn Chunchon South Korea

2D-to-3D conversion has been studied over past decades and integrated to commercial 3D displays and 3DTVs. Generally, depth cues extracted from a static image are used for generating a depth map followed by depth image-based rendering for producing a stereoscopic image. Further, the motion has been considered as an important cue for motion depth estimation. In most works, motion estimation has relied on block-based motion estimation, optical flows, and their variants even though they provide inaccurate data and high computation time, posing performance bottleneck. These problems by proposing Motion History Image-based motion depth estimation method are addressed. Experimental results show that the proposed method is not only faster than the conventional methods but also outperforms them in terms of motion depth estimation.

关键词： video signal processing motion estimation image sequences stereo image processing three-dimensional displays rendering (computer graphics) performance bottleneck computation time optical flows block-based motion estimation stereoscopic image depth image-based rendering depth map generation depth cues extraction 3DTV commercial 3D displays 2D-to-3D video conversion motion history image-based motion depth estimation method MHI motion depth generation

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Strategic Bidding for Producers in Nodal Electricity Markets: A Convex Relaxation Approach

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第3期32卷 2324-2336页

作者： Ghamkhari, Mahdi Sadeghi-Mobarakeh, Ashkan Mohsenian-Rad, Hamed Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA

Strategic bidding problems in electricity markets are widely studied in power systems, often by formulating complex bi-level optimization problems that are hard to solve. The state-of-the-art approach to solve such problems is to reformulate them as mixed-integer linear programs (MILPs). However, the computational time of such MILP reformulations grows dramatically, once the network size increases, scheduling horizon increases, or randomness is taken into consideration. In this paper, we take a fundamentally different approach and propose effective and customized convex programming tools to solve the strategic bidding problem for producers in nodal electricity markets. Our approach is inspired by the Schmudgen's Positivstellensatz Theorem in semi-algebraic geometry;but then we go through several steps based upon both convex optimization and mixed-integer programming that results in obtaining close to optimal bidding solutions, as evidenced by several numerical case studies, besides having a huge advantage on reducing computation time. While the computation time of the state-of-the-art MILP approach grows exponentially when we increase the scheduling horizon or the number of random scenarios, the computation time of our approach increases rather linearly.

关键词： computation time convex optimization equilibrium constraints nodal electricity market strategic bidding

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Distributed Economic Model Predictive Control for Operational Safety of Nonlinear Processes

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AICHE JOURNAL 2017年第8期63卷 3404-3418页

作者： Albalawi, Fahad Durand, Helen Christofides, Panagiotis D. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA

Achieving operational safety of chemical processes while operating them in an economically-optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety-based constraints within model predictive control (MPC) design;however, the safety-based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety-based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov-based economic model predictive control (LEMPC) formulated with safety-based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed-loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. (C) 2017 American Institute of Chemical Engineers

关键词： distributed economic model predictive control process safety computation time

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Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

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IET IMAGE PROCESSING 2017年第12期11卷 1188-1196页

作者： Bi, Hui Tang, Hui Yang, Guanyu Li, Baosheng Shu, Huazhong Dillenseger, Jean-Louis Southeast Univ Sch Comp Sci & Engn Lab Image Sci & Technol Nanjing Jiangsu Peoples R China Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Jiangsu Peoples R China Shandong Canc Hosp Dept Radiat Oncol Jinan Shandong Peoples R China Ctr Rech Informat Biomed Sino Francais CRIBs Nanjing Jiangsu Peoples R China Univ Rennes 1 Lab Traitement Signal & Image Rennes France

As a particular case of the finite mixture model, Rayleigh mixture model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. The authors proposed an improved RMM with neighbour (RMMN) information to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. They evaluated their model on experiments on synthetic data and real US images used by high-intensity focused ultrasound therapy. On this data, they demonstrated that the proposed model outperforms several state-of-the-art methods in terms of both segmentation accuracy and computation time.

关键词： ultrasonic imaging image segmentation medical image processing biomedical ultrasonics spatial information Rayleigh mixture model fast segmentation finite mixture model medical ultrasound image segmentation intensity distribution inhomogeneous regions improved RMM-neighbour information improved RMMN information mean template spatial information incorporation window size local gradient distribution high-intensity focused ultrasound therapy segmentation accuracy computation time

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Distributed Economic MPC with Safety-Based Constraints for Nonlinear Systems

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IFAC-PapersOnLine 2017年第1期50卷 12033-12040页

作者： Albalawi, Fahad Durand, Helen Christofides, Panagiotis D. Department of Electrical Engineering University of California Los AngelesCA90095 United States Department of Chemical and Biomolecular Engineering University of California Los AngelesCA90095 United States Department of Chemical and Biomolecular Engineering Department of Electrical Engineering University of California Los AngelesCA90095 United States

Promoting process safety of chemical processes while operating them in an economically-optimal manner is a matter of great importance. In Albalawi et al. (2016), a safety-based economic model predictive control methodology (safety-EMPC) was developed to operate nonlinear processes in an economically-optimal manner while maintaining process safety and closed-loop stability. However, the safety-EMPC control strategy was developed with a centralized economic model predictive control (EMPC) structure; thus, computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. Alternatively, we develop in this work sequential and iterative safety-based distributed EMPC schemes (safety-DEMPC) that may overcome the computation time limitations of the centralized safety-EMPC while maintaining similar closed-loop performance. Using a catalytic reactor example, the two proposed safety-DEMPC schemes were demonstrated to achieve similar closed-loop performance to the centralized safety-EMPC while reducing the on-line computation time requirements compared to the centralized safety-EMPC. © 2017

关键词： Model predictive control Controllers Iterative methods Predictive control systems Closed loop stability Closed loop performance computation time Control strategies Controller designs Distributed Model predictive Control Online computations Process safety

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computation Efficient Attribute Based Broadcast Group Key Management for Secure Document Access in Public Cloud

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2017年第3期33卷 695-712页

作者： Pandiaraja, Perumal Vijayakumar, Pandi Vijayakumar, Varadarajan Seshadhri, Raman Arunai Engn Coll Dept Comp Sci & Engn Thiruvannamalai 606603 India Univ Coll Engn Tindivanam Dept Comp Sci & Engn Melpakkam 604001 Tamil Nadu India VIT Univ Sch Comp Sci & Engn Chennai Campus Madras 600127 Tamil Nadu India GAVS Technol Pvt Ltd Madras 600119 Tamil Nadu India

An efficient fine-grained encryption-based access control scheme for documents stored in the public cloud network proposed by Mohamed et al. utilizes more computational and storage complexities. Although, Mohamed et al.'s broadcast key management scheme communicates group key securely, it consumes massive computational and storage resources for frequently changing the group key. In order to avoid these problems, we have proposed a new broadcast group key management scheme that takes reduced computational resources by performing minimal cryptographic operations in the data owner and cloud user's to update and recover the group key using a matrix called access control vector. The computation complexity of the data owner is reduced by performing simple arithmetic operations for updating the group key. Moreover, the computational complexity of the cloud user is also minimized by performing only one addition and two subtraction operations whenever there is a change in the group or access control policy. In addition, the proposed scheme also minimizes the storage complexity of cloud users, but maintains the same communication complexity as that of Mohamed et al.'s scheme. The performance results show that the proposed scheme is computationally efficient in the data owner and the cloud users.

关键词： access controls cryptographic controls data encryption data sharing computation time

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Planning Dragline Positioning Sequence with A*Search Algorithm

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IFAC-PapersOnLine 2017年第1期50卷 12477-12483页

作者： Liu, Haoquan Kearney, Michael Forbes, Michael School of Mechanical and Mining Engineering The University of Queensland BrisbaneQLD4072 Australia School of Mathematics and Physics The University of Queensland BrisbaneQLD4072 Australia

Dragline excavators are among the largest earthmoving machines in surface mining, where they are used to remove the waste material that sits above a target mineral or coal reserve. The effectiveness of their operation is highly dependent on the sequence of positions at which they operate and the material movement during the excavation at each position. This paper addresses the problem of how to position a dragline in order to efficiently finish an excavation task with a specified digging and dumping strategy at each position. The dragline positioning sequence is computed among a range of candidate positions using the A* search algorithm. The algorithm is applied to two simulated dragline excavation scenarios that are defined based on actual terrain data. We show that the proposed A* algorithm is able to produce the optimal positioning sequences in a reasonable amount of computation time, allowing it to be applied in real-time decision support for the dragline operators. © 2017

关键词： Excavation Artificial intelligence Coal Decision making Decision support systems Learning algorithms Optimization Proven reserves Candidate positions Coal reserves computation time Dragline operation Earth moving machines Optimal positioning Real time decisions Search Algorithms

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