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Robust Nanoparticles Detection From Noisy Background by Fusing Complementary Image Information

IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第12期26卷 5713-5726页

作者： Qian, Yanjun Huang, Jianhua Z. Li, Xiaodong Ding, Yu Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

This paper studies the problem of detecting the presence of nanoparticles in noisy transmission electron microscopic (TEM) images and then fitting each nanoparticle with an elliptic shape model. In order to achieve robustness while handling low contrast and high noise in the TEM images, we propose an approach to fuse two kinds of complementary image information, namely, the pixel intensity and the gradient (the first derivative in intensity). Our approach entails two main steps: 1) the first step is to, after necessary pre-processing, employ both intensity-based information and gradient-based information to process the same TEM image and produce two independent sets of results and 2) the subsequent step is to formulate a binary integer programming (BIP) problem for conflict resolution among the two sets of results. Solving the BIP problem determines the final nanoparticle identification. We apply our method to a set of TEM images taken under different microscopic resolutions and noise levels. The empirical results show the merit of the proposed method. It can process a TEM image of 1024 x 1024 pixels in a few minutes, and the processed outcomes appear rather robust.

关键词： binary integer programming complementary information fusion image segmentation multi-expert system nanoparticle analysis

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Minimum Cost Multi-Way Data Association for Optimizing Multitarget Tracking of Interacting Objects

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015年第3期37卷 611-624页

作者： Park, Chiwoo Woehl, Taylor J. Evans, James E. Browning, Nigel D. Florida State Univ Dept Ind & Mfg Engn Tallahassee FL 32310 USA Ames Natl Lab Div Mat Sci & Engn Ames IA USA Pacific NW Natl Lab Dept Environm & Mol Sci Richland WA USA Pacific NW Natl Lab Dept Fundamental & Computat Sci Richland WA 99352 USA

This paper presents a general formulation for a minimum cost data association problem which associates data features via one-to-one, m-to-one and one-to-n links with minimum total cost of the links. A motivating example is a problem of tracking multiple interacting nanoparticles imaged on video frames, where particles can aggregate into one particle or a particle can be split into multiple particles. Many existing multitarget tracking methods are capable of tracking non-interacting targets or tracking interacting targets of restricted degrees of interactions. The proposed formulation solves a multitarget tracking problem for general degrees of inter-object interactions. The formulation is in the form of a binary integer programming problem. We propose a polynomial time solution approach that can obtain a good relaxation solution of the binary integer programming, so the approach can be applied for multitarget tracking problems of a moderate size (for hundreds of targets over tens of time frames). The resulting solution is always integral and obtains a better duality gap than the simple linear relaxation solution of the corresponding problem. The proposed method was validated through applications to simulated multitarget tracking problems and a real multitarget tracking problem.

关键词： Data association binary integer programming decomposition lagrange dual relaxation

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On channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2013年第4期12卷 225-244页

作者： Jahanshahi, Mohsen Dehghan, Mehdi Meybodi, Mohammad Reza Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran

Multicast is a key networking service, enabling one-shot delivery of information from a source to multiple destinations and is considered underlying for collaborating multimedia applications such as video conferencing, distance learning and other forms of content distributing over Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR WMNs). Multicast protocol as designed in these networks, however, is tightly coupled with the specifics of the nodes' channel-radio associations to realise minimum interference communication. The mainstream of research in WMN multicasting is oriented towards heuristic or meta-heuristic strategies which basically take on a sequential approach to solve the channel assignment and the multicast routing as two disjoint sub-problems. The resulting network configurations would be sub-optimal in this case. It is given that the cross-interaction between the two sub-problems is an effect of the problem's specifications. In this paper, first, we propose a cross-layer mathematical formulation of joint channel assignment and multicast tree construction in MCMR WMNs, which, opposed to the existing schemes guarantees optimal solution. The simulation results demonstrate that our cross-layer design outperforms the Level Channel Assignment (LCA), Multi-Channel Multicast (MCM), the Genetic Algorithm (GA), Simulated Annealing (SA) and the Tabu Search (TS)-based methods proposed by Zeng et al. (2010) and Cheng et al. (2011) respectively, in terms of inter-channel interference. Second, since joint optimisation modelling has been relatively demanding in terms of complexity, we relax the optimality requirement and alternatively explore the option of a layered formulation in which to ensure an optimal solution for each sub-problem. Our alternative design is proved superior to the prior art in terms of interference minimisation too. We conduct an extensive series of simulations to analyse the optimality and complexity of our two design strategies. The overall r

关键词： WMN wireless mesh network multi-channel multi-radio channel assignment multicast tree construction cross-layer design BIP binary integer programming

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A PMU Placement Scheme Ensuring Real-Time Monitoring of Critical Buses of the Network

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IEEE TRANSACTIONS ON POWER DELIVERY 2014年第2期29卷 510-517页

作者： Pal, Anamitra Sanchez-Ayala, Gerardo A. Centeno, Virgilio A. Thorp, James S. Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

This paper presents a phasor measurement unit (PMU) placement scheme that provides real-time monitoring of key buses of the network. High-voltage lines, substations relevant for transient and dynamic stability of the network, and buses with high connectivity are given the highest priority while placing the PMUs. binary integer programming and "depth of unobservability" are used to find the relevant PMU placement set. The placement scheme has been tested on the IEEE 118-bus system, IEEE 300-bus system, a 283-bus model of the Central American Power Transmission System, and a complex 996-bus network describing the Northern and the Eastern power grids of India. The results indicate that the proposed technique will be useful to utilities that want to initially protect the most important buses of their system on their way to attaining complete observability.

关键词： binary integer programming observability phasor measurement units (PMUs) redundancy wide-area measurement system (WAMS)

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Optimal Camera Network Configurations for Visual Tagging

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2008年第4期2卷 464-479页

作者： Zhao, Jian Cheung, Sen-Ching (Samson) Nguyen, Thinh Univ Kentucky Dept Elect & Comp Engn Ctr Visualizat & Virtual Environm Lexington KY 40507 USA Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Proper placement of cameras in a distributed smart camera network is an important design problem. Not only does it determine the coverage of the surveillance, but it also has a direct impact on the appearance of objects in the cameras which dictates the performance of all subsequent computer vision tasks. In this paper, we propose a generic camera placement model based on the visibility of objects at different cameras. Our motivation stems from the need of identifying and locating objects with distinctive visual features or "tags." This is a very common goal in computer vision with applications ranging from identifying soccer players by their jersey numbers to locating and recognizing faces of individuals. Our proposed framework places no restriction on the visual classification tasks. It incorporates realistic camera models, self occlusion of tags, and occlusion by other moving objects. It is also flexible enough to handle arbitrary-shaped three-dimensional environments. Using this framework, two novel binary integer programming (BIP) algorithms are proposed to find the optimal camera placement for "visual tagging" and a greedy implementation is developed to cope with the complexity of BIP. Extensive performance analysis is performed using Monte Carlo simulations, virtual environment simulations, and real-world experiments. We also demonstrate the usefulness of visual tagging through robust individual identification and obfuscation across multiple camera views for privacy protection.

关键词： binary integer programming camera placement multiple view geometry privacy protection smart camera network visual tags

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Research on selection strategy of machining equipment in cloud manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期71卷 1549-1563页

作者： Wang, Shi-long Guo, Liang Kang, Ling Li, Chang-song Li, Xiao-yong Stephane, Yossanguem Madjinoudji Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Cloud manufacturing (CM) is a new type of networked manufacturing model, which is proposed in 2010. Optimization technology is one of the key techniques for CM operation, which are used for the efficient integration of manufacturing resources. In all kinds of manufacturing resources, the machining equipment is one of the most important resources. Using optimization techniques to achieve optimal selection of machining equipment is rarely studied in the CM. In order to handle the optimization selection of machining equipment in CM, comparing with the existing resources optimal configuration, an optimal selection strategy is introduced for the machining equipment in CM. In the selection strategy, first, a multiple objective and binary integer programming model is proposed to describe the optimal selection of machining equipment in CM. Second, after analyzing the mathematical model and the real-world problem of the machining equipment selection in CM, the priority method is adopted to convert the multiple-objective problem into a single-objective problem. Third, an improved particle swarm optimization (IPSO) algorithm based on a novel encoding scheme and fitness function is presented to solve the single-objective mathematical model. Finally, the simulation experiments verify the effectiveness of the IPSO algorithm and show that the selection strategy is more objective and effective to help the client select the machining equipment in the CM than current resources optimization model. This research provides a theoretical support for the development of CM.

关键词： Cloud manufacturing Machining equipment selection Particle swarm optimization Multiple objective programming binary integer programming

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Knowledge granularity reduction for decision tables

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第3期13卷 569-577页

作者： Liu, Guilong Feng, Yanbin Beijing Language & Culture Univ Sch Informat Sci Beijing Peoples R China

Attribute reduction is a difficult topic in rough set theory and knowledge granularity reduction is one of the important types of reduction. However, up to now, its reduction algorithm based on a discernibility matrix has not been given. In this paper, we show that knowledge granularity reduction is equivalent to both positive region reduction and X-absolute reduction, and derive its corresponding algorithm based on a discernibility matrix to fill the gap. Particularly, knowledge granularity reduction is the usual positive region reduction for consistent decision tables. Finally, we provide a simple knowledge granularity reduction algorithm for finding a reduct with the help of binary integer programming, and consider six UCI datasets to illustrate our algorithms.

关键词： Attribute reduction binary integer programming Decision table Discernibility matrix Knowledge granularity

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A comparative study of object-level spatial context techniques for semantic image analysis

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COMPUTER VISION AND IMAGE UNDERSTANDING 2011年第9期115卷 1288-1307页

作者： Papadopoulos, G. Th Saathoff, C. Escalante, H. J. Mezaris, V. Kompatsiaris, I. Strintzis, M. G. Aristotle Univ Thessaloniki Dept Elect & Comp Engn GR-54124 Thessaloniki Greece Ctr Res & Technol Hellas CERTH ITI GR-57001 Thessaloniki Greece Univ Koblenz Landau WeST Inst Web Sci & Technol D-56070 Koblenz Germany Univ Autonoma Nuevo Leon Grad Program Syst Engn San Nicolas De Los Garza 66450 NL Mexico

In this paper, three approaches to utilizing object-level spatial contextual information for semantic image analysis are presented and comparatively evaluated. Contextual information is in the form of fuzzy directional relations between image regions. All techniques, namely a Genetic Algorithm (GA), a binary integer programming (BIP) and an Energy-Based Model (EBM), are applied in order to estimate an optimal semantic image interpretation, after an initial set of region classification results is computed using solely visual features. Aim of this paper is the in-depth investigation of the advantages of each technique and the gain of a better insight on the use of spatial context. For this purpose, an appropriate evaluation framework, which includes several different combinations of low-level features and classification algorithms, has been developed. Extensive experiments on six datasets of varying problem complexity have been conducted for investigating the influence of typical factors (such as the utilized visual features, the employed classifier, and the number of supported concepts) on the performance of each spatial context technique, while a detailed analysis of the obtained results is also given. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Spatial context Directional relations Semantic image analysis Genetic algorithm binary integer programming Energy-based model

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Embedding optimization with deterministic discrete event simulation for assignment of cross-trained operators: An assembly line case study

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COMPUTERS & OPERATIONS RESEARCH 2019年 111卷 99-115页

作者： Dagkakis, Georgios Rotondo, Anna Heavey, Cathal Nexedi SA 147 Rue Ballon F-59110 Lille France Irish Mfg Res Aerodrome Business Pk Rathcoole Ireland Univ Limerick Enterprise Res Ctr Sch Engn Limerick Ireland

In manufacturing systems where productivity is constrained by operators' availability, cross-training strategies can be used to enable dynamic assignment of operators to workstations. However, finding an assignment approach that efficiently works under various system conditions is not trivial as, among other factors, the level of cross-training, the production duration and the initial conditions of the system can influence the assignment approach's performance. To overcome this issue, in the case study presented in this paper, operator assignments have been modeled using a simulation-based optimization approach, with an "outer" optimizer that selects assignment-related parameters to simulate based on the system conditions, and an "inner" optimizer integrated with a simulation model that generates optimal assignments. For the case described here, which is modeled using a deterministic simulation model, the "outer" optimizer is an Ant Colony Optimizer (ACO) and the "inner" optimizer is a binary integer programming (BIP) model. The ACO will select weights for the assignment objectives of the BIP multi-objective function so that throughput is maximized. The BIP is called by the system simulation model at fixed intervals or when a system status changes to assign operators to workstations. Results show that the simulation based optimization approach generates higher throughput performance than static WIP-base assignment, especially when longer production duration are considered. The effects of cross-training and production duration on production throughput are also investigated. The simulation-optimization approach used can be abstracted to a framework where the "outer" and "inner" optimizers may be applied to different domains than the case study addressed here and can also be applied to stochastic simulation models. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Operator assignment Simulation-optimization Ant colony optimization binary integer programming

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An optimal control model for load shifting - With application in the energy management of a colliery

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APPLIED ENERGY 2009年第7-8期86卷 1266-1273页

作者： Middelberg, Arno Zhang, Jiangfeng Xia, Xiaohua Univ Pretoria Dept Elect Elect & Comp Engn Ctr New Energy Syst ZA-0002 Pretoria South Africa

This paper presents an optimal control model for the load shifting problem in energy management and its application in a South African colliery. it is illustrated in the colliery scenario that how the optimal control model can be applied to optimize load shifting and improve energy efficiency through the control of conveyor belts. The time-of-use electricity tariff is used as an input to the objective function in order to obtain a solution that minimizes electricity costs and thus maximizes load shifting. The case study yields promising results that show the potential of applying this optimal control model to other industrial Demand Side Management initiatives. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Load shifting Optimal control Energy management Demand Side Management Time-of-use Dense Media Separation binary integer programming

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