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Fusion of Extreme Learning Machine and graph-based optimization Methods for Active Classification of Remote Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2015年第3期12卷 527-531页

作者： Bencherif, Mohamed A. Bazi, Yakoub Guessoum, Abderrezak Alajlan, Naif Melgani, Farid AlHichri, Haikel Saad Dahlab Univ Fac Technol Dept Elect Blida 9000 Algeria King Saud Univ Coll Comp & Informat Sci Dept Comp Engn ALISR Lab Riyadh 11543 Saudi Arabia Univ Trento Dept Informat Engn & Comp Sci I-38213 Trento Italy

In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.

关键词： Active learning (AL) extreme learning machine (ELM) graph-based optimization multiclass classification

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A Multi-User Personal Indoor Localization System Employing graph-based optimization

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SENSORS 2019年第1期19卷 157-157页

作者： Nowicki, Michal R. Skrzypczynski, Piotr Poznan Univ Tech Inst Control Robot & Informat Engn PL-60965 Poznan Poland

Personal indoor localization with smartphones is a well-researched area, with a number of approaches solving the problem separately for individual users. Most commonly, a particle filter is used to fuse information from dead reckoning and WiFi or Bluetooth adapters to provide an accurate location of the person holding a smartphone. Unfortunately, the existing solutions largely ignore the gains that emerge when a single localization system estimates locations of multiple users in the same environment. Approaches based on filtration maintain only estimates of the current poses of the users, marginalizing the historical data. Therefore, it is difficult to fuse data from multiple individual trajectories that are usually not perfectly synchronized in time. We propose a system that fuses the information from WiFi and dead reckoning employing the graph-based optimization, which is widely applied in robotics. The presented system can be used for localization of a single user, but the improvement is especially visible when this approach is extended to a multi-user scenario. The article presents a number of experiments performed with a smartphone inside an office building. These experiments demonstrate that graph-based optimization can be used as an efficient fusion mechanism to obtain accurate trajectory estimates both in the case of a single user and in a multi-user indoor localization system. The code of our system together with recorded dataset will be made available when the paper gets published.

关键词： indoor positioning graph-based optimization WiFi fingerprinting

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Super-process interoperability optimization architecture in healthcare ultra-large-scale systems: A graph-based multi-objective approach

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第3期34卷

作者： Shirazi, Babak Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran

The healthcare industry is more challenging and the super-process interoperability should be planned in order to architecture an informational technology structure of the required data for progression of care. Large-scale enterprises could be provided cross-organization super-processes operations. Therefore, each enterprise's business process management system (BPMS) may be executed cross-organizational BPM as a kind of the process model. This study proposed a Super-process Interoperable Optimized Architecture based on BPMS as a paradigm to satisfy the consistency of cross-organization operations in healthcare industry. For developing the healthcare ultra-large-scale process management system (H-ULS-PMS), the paper provided interoperability optimization with intelligent recommender system in estimation component of BPMS that has been altered to H-ULS-PMS. This system could be recommended the appropriate concept of crossed healthcare process according to the hospitals' requirements. A graph-based multi-objective optimization method has been applied to control distributed resources across the hospitals. The community cloud is used as proposed architecture infrastructure for largely scaled interoperability. A practical case in hospital as a research project of a hospital in Dusseldorf-Wersten of Germany has been studied to show the better interoperability and resource management of the proposed super-process interoperability architecture.

关键词： graph-based optimization healthcare ultra-large-scale process management system interoperability resource management super-process interoperable optimized architecture

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Multiagent Coordination Using graph Structured Mathematical optimization 16

Multiagent Coordination Using Graph Structured Mathematical ...

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16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Singh, Arambam James Kumar, Akshat Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781510855076

We address the problem of solving mathematical programs defined over a graph where nodes represent agents and edges represent interaction among agents. We focus on the class of graph structured linear and quadratic programs (LPs/QPs) which can model important multiagent coordination frameworks such as distributed constraint optimization (DCOP). For DCOPs, our framework provides a key benefit of modelling functional constraints among agents (e.g. resource, network flow constraints) in a much more tractable fashion. Our framework is also more general than previous work on solving graph-based LPs/QPs as it can model a richer class of objective function and constraints than previous work. Our iterative approach has several desirable properties-it is guaranteed to converge to the optimal solution for LPs, it works for general cyclic graphs, it is memory efficient making it suitable for resource limited agents, and has anytime property. Empirically, our approach provides solid empirical results on several standard benchmark problems when compared against previous approaches.

关键词： DCOP Congestion-Games graph-based optimization

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Auto-adaptation of Genetic Operators for Multi-objective optimization in the Firefighter Problem

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15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (纸本)9783319108407;9783319108391

In the firefighter problem the spread of fire is modelled on an undirected graph. The goal is to find such an assignment of firefighters to the nodes of the graph that they save as large part of the graph as possible. In this paper a multi-objective version of the firefighter problem is proposed and solved using an evolutionary algorithm. Two different auto-adaptation mechanisms are used for genetic operators selection and the effectiveness of various crossover and mutation operators is studied.

关键词： operator auto-adaptation multi-objective evolutionary optimization graph-based optimization firefighter problem

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Reducing systemic risk in multiplex networks using evolutionary optimization 17

Reducing systemic risk in multiplex networks using evolution...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Dept Informat Technol Inst Business Informat Komandorska 118-120 PL-53345 Poland Poland

ISBN: (纸本)9781450349390

This paper concerns the topic of cascading failures in a system containing economic entities. Cascading failures occur when some of the entities fail and, because of the relationships on the market, cause many more entities to fail. In this paper a multiobjective optimization problem is formulated for an economic model involving companies composed of a core-business and a subsidiary-business unit. A multiobjective optimization algorithm is used to solve the optimization problem, thereby making the system more robust against failures. Subsequently, the properties of the optimized system are studied. Solving the proposed optimization problem diminishes the severity of cascading failures occurring in the system and increases the threshold coupling strength between business units for which the cascading failure occurs.

关键词： graph-based optimization Multi-objective evolutionary optimization Cascading failures

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Background-Driven Salient Object Detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第4期19卷 750-762页

作者： Wang, Zilei Xiang, Dao Hou, Saihui Wu, Feng Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol West Campus Hefei 230027 Peoples R China

The background information is a significant prior for salient object detection, especially when images contain cluttered background and diverse object parts. In this paper, we propose a background-driven salient object detection (BD-SOD) method to more comprehensively exploit the background prior, aiming at generating more accurate and robust salient maps. To be specific, we first exploit the background prior to conduct the saliency estimation, i.e., computing the regional saliency values. In this stage, the background prior is utilized in threefold: restricting the reference regions to only the background regions, weighting the contribution of reference regions, and leveraging the importance of different features. Benefiting from such an explicit utilization, the proposed model can greatly mitigate the negative interference of the cluttered background and diverse object parts. We then embed the background prior into the optimization graph for saliency refinement. Specifically, two virtual supernodes (representing the background and foreground, respectively) are introduced with extra connections, and the nonlocal feature connections between similar regions are also set up. These connections enhance the power of optimization graph to alleviate the perturbations from diverse parts, and thus help to achieve the uniformity of saliency values. Finally, we provide systematical studies to investigate the effectiveness of the proposed BD-SOD in exploiting the valuable background prior. Experimental results on multiple public benchmark datasets, including MSRA-1000, THUS-10000, PASCAL-S, and ECSSD, clearly show that BD-SOD consistently outperforms the well-established baselines and achieves state-ofthe- art performance.

关键词： Salient object detection background prior saliency estimation graph-based optimization

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Accurate and Robust Visual Localization System in Large-Scale Appearance-Changing Environments

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第6期27卷 5222-5232页

作者： Yu, Yang Yun, Peng Xue, Bohuan Jiao, Jianhao Fan, Rui Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Dept Elect & Comp Engn Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res I Shenzhen Peoples R China

Localization in large-scale environments with robust performance is a persistent challenge for mobile robots. This article proposes a novel system to achieve accurate and robust visual localization performance in largescale environments with appearance-changing surroundings. Our system starts from a stage of extracting stable visual features with an object segmentation network. After measurement postprocessing and extrinsic precalibration, we propose a graph-based optimization module to estimate the optimal pose as well as extrinsics. We construct optimization constraints with refined wheel odometry, feature matching between images, and correspondences between images and the prebuild map. We evaluate our segmentation module on our proposed datasets and test our localization module with seven sequences (9.8 km total length) in real port scenes with different working conditions from day to night and sunny to rainy. Experiment results demonstrate the decimeter-level accuracy and robust performance of our approach in various challenging scenarios, showing competitive performance compared with state-of-theart LiDAR-based localization methods.

关键词： graph-based optimization neural network online calibration visual localization

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Automatic generation of efficient and interference-free five-axis scanning path for free-form surface inspection

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COMPUTER-AIDED DESIGN 2018年 98卷 24-38页

作者： Hu, Pengcheng Zhou, Huicheng Chen, Jihong Lee, Chenhan Tang, Kai Yang, Jianzhong Shen, Shuyu Huazhong Univ Sci & Technol Natl Numer Control Syst Engn Res Ctr Wuhan Hubei Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China

As a new contacting surface inspection technology, five-axis continuous sweep scanning enables a much higher inspection efficiency compared with the traditional three-axis point-by-point inspection. However, currently how to plan an interference-free scanning path for an arbitrary free-form surface still heavily depends on human intervention and the scanning path thus obtained lacks severely in inspection efficiency since the unique kinematic characteristics of the five-axis inspection are ignored. Motivated by these deficiencies, this paper proposes a practical algorithm for automatically generating an interference free and efficient five-axis continuous scanning path for an arbitrary free-form surface. The algorithm adopts the guiding-path + trajectory-curve paradigm for the generation of scanning curves while the collision-free constraint on the stylus is taken care of by considering the aggregate accessibility of the reference curves. A digraph is then constructed on the aggregated accessibility domains of the sample points of the guiding path and the stylus orientation along the guiding path is determined by finding the shortest path in this graph. A simple surface partitioning scheme is also introduced in case a surface cannot be scanned in a single pass and needs to be divided into suitable smaller patches. Preliminary experiments in both computer simulation and physical inspection of the proposed algorithm validate that, when compared with the two leading commercial solutions, the proposed algorithm not only is fully automatic without relying on human intervention but also achieves a much higher inspection efficiency. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Continuous scanning path Interference-free Surface partitioning graph-based optimization

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ED-LS - A heuristic local search for the multiobjective Firefighter Problem

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APPLIED SOFT COMPUTING 2017年 59卷 389-404页

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

One of the approaches to combinatorial optimization is to use global search methods, such as evolutionary algorithms combined with local search procedures. Local search can be very effective in improving the quality of solutions, however, searching large neighbourhoods can be computationally expensive. In this paper a new local search method is described that is dedicated to the Firefighter Problem (FFP). The Firefighter Problem concerns protecting nodes of a graph in order to stop a threat that is spreading in the graph. Because the resources used for protection are limited, the goal is to find the best order in which the nodes are protected and thus the FFP is a combinatorial optimization problem. One of the key elements in the design of a local search method is the definition of the neighbourhoods of solutions from which the local search starts. The method proposed in this paper (ED-LS) aims at lowering the computational cost of the local search by reducing, based on certain heuristics, the size of these neighbourhoods. In this paper the ED-LS is compared to an optimization without the local search, a local search using non-reduced neighbourhoods and a method that reduces the neighbourhood by checking only some randomly selected neighbours. The results show that the ED-LS improves the performance of the algorithm with respect to no local search at all and to the local search using non-reduced neighbourhoods, supporting the view that reducing the size of the neighbourhood is beneficial. Also, the ED-LS produces better results than when the size of the neighbourhood is reduced randomly, which means that the proposed heuristics are indeed selecting promising neighbours effectively. In the last part of the experiments the ED-LS is further improved to obtain faster improvement of solutions at the beginning of the optimization run. (C) 2017 Elsevier B.V. All rights reserved.

关键词： graph-based optimization Multiobjective optimization Combinatorial problems

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