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< Page ,Total 29 >
Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights EI SCIE Scopus
期刊论文 | 2020 , 2020 | COMPLEXITY | IF: 2.833
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Abstract :

In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights.

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GB/T 7714 Shang, Ke , Chan, Felix T. S. , Karungaru, Stephen et al. Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights [J]. | COMPLEXITY , 2020 , 2020 .
MLA Shang, Ke et al. "Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights" . | COMPLEXITY 2020 (2020) .
APA Shang, Ke , Chan, Felix T. S. , Karungaru, Stephen , Terada, Kenji , Feng, Zuren , Ke, Liangjun . Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights . | COMPLEXITY , 2020 , 2020 .
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Route-reduction-based dynamic programming for large-scale satellite range scheduling problem EI Scopus SCIE
期刊论文 | 2019 , 51 (11) , 1944-1964 | Engineering Optimization | IF: 2.165
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

Satellites offer many services through communication with stations, such as tracking, navigation, telecommand uplink, earth observation, etc. How to coordinate these services is referred to as the satellite range scheduling problem (SRSP). In the research, it is found that only the resources (referring to time slots of stations) requested by more than one satellite simultaneously influence scheduling results. These resources are called critical resources and selected as scheduling elements, which makes some jobs optimally served in advance and the problem be decomposable into a multi-stage decision process, so dynamic programming is suitable to be employed. For large-scale SRSPs, a route-reduction-based dynamic programming (RR-DP) is presented, wherein a multi-level route reduction strategy is adopted to alleviate ‘the curse of dimensionality’. Experimental results reveal that RR-DP can find optimal solutions for small-to-medium sized problems and outperforms state-of-the-art methods for large-scale problems. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Keyword :

Dynamic programming Communication satellites Satellites Combinatorial optimization Scheduling

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GB/T 7714 Liu, Zhenbao , Feng, Zuren , Ren, Zhigang . Route-reduction-based dynamic programming for large-scale satellite range scheduling problem [J]. | Engineering Optimization , 2019 , 51 (11) : 1944-1964 .
MLA Liu, Zhenbao et al. "Route-reduction-based dynamic programming for large-scale satellite range scheduling problem" . | Engineering Optimization 51 . 11 (2019) : 1944-1964 .
APA Liu, Zhenbao , Feng, Zuren , Ren, Zhigang . Route-reduction-based dynamic programming for large-scale satellite range scheduling problem . | Engineering Optimization , 2019 , 51 (11) , 1944-1964 .
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An advanced bispectrum features for EEG-based motor imagery classification EI SCIE Scopus
期刊论文 | 2019 , 131 , 9-19 | EXPERT SYSTEMS WITH APPLICATIONS | IF: 5.452
WoS CC Cited Count: 15 SCOPUS Cited Count: 18
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Motor imagery (MI)-related brain activities can be effectively described by frequency analysis. Bispectrum is developed to overcome the drawback of power spectrum that the estimation of power spectrum discards the phase relationship among frequency components. However, the widely used bispectral features extraction method adds up all bispectral values as one feature, which could lead to the loss of effective information and increase of the sensitivity to non-linear and non-Gaussian noises. Thus, the representative bispectral features extraction method may be inefficient for MI classification. In addition, recent research suggests that the variations of EEG signals could provide more useful underlying information of event-related brain responses. This paper presents an advanced variations based bispectral feature extraction method to improve the performance of MI classification. The proposed method calculates the variations of MI-related EEG signals as input to bispectrum estimation. Besides, a new segmented bispectral sum features are developed to reduce the influence of non-linear and non-Gaussian noises and emphasize the valuable information for MI classification. The dataset collected in our laboratory and BCI Competition IV dataset 2b were adopted to validate the proposed method. The results indicate that the proposed method outperforms the power spectrum based methods and the representative bispectral features based methods. Moreover, compared to other state-of-the-art works, our approach also achieves the greater performance for MI classification. (C) 2019 Elsevier Ltd. All rights reserved.

Keyword :

Motor imagery Variations Bispectrum Event-related potentials Classification Features extraction

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GB/T 7714 Sun, Lei , Feng, Zuren , Lu, Na et al. An advanced bispectrum features for EEG-based motor imagery classification [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2019 , 131 : 9-19 .
MLA Sun, Lei et al. "An advanced bispectrum features for EEG-based motor imagery classification" . | EXPERT SYSTEMS WITH APPLICATIONS 131 (2019) : 9-19 .
APA Sun, Lei , Feng, Zuren , Lu, Na , Wang, Beichen , Zhang, Wenjun . An advanced bispectrum features for EEG-based motor imagery classification . | EXPERT SYSTEMS WITH APPLICATIONS , 2019 , 131 , 9-19 .
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Video Anomaly Detection Using Ensemble One-Class Classifiers EI Scopus CPCI-S
会议论文 | 2018 , 2018-July , 9343-9349
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© 2018 Technical Committee on Control Theory, Chinese Association of Automation. In this paper we present a novel algorithm for video anomaly detection. It is based on multiple local cells, which are acquired by splitting entire monitor scene. At each local cell, we group all feature vectors with clustering algorithm based on minimum spanning tree, and further model all groups using improved one-class SVM to build ensemble classifiers. For any new features at each local node in incoming video clips, we use the corresponding learned ensemble classifiers to estimate maximum abnormality degree. The proposed approach has been tested on publicly available datasets with frame-level and pixel-level criteria, and outperforms other state-of-the-art approaches.

Keyword :

Anomaly detection Clustering algorithm Minimum spanning tree One-class classifier

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GB/T 7714 Li, Gang , Feng, Zuren , Lv, Na . Video Anomaly Detection Using Ensemble One-Class Classifiers [C] . 2018 : 9343-9349 .
MLA Li, Gang et al. "Video Anomaly Detection Using Ensemble One-Class Classifiers" . (2018) : 9343-9349 .
APA Li, Gang , Feng, Zuren , Lv, Na . Video Anomaly Detection Using Ensemble One-Class Classifiers . (2018) : 9343-9349 .
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Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (2) | SENSORS | IF: 3.031
WoS CC Cited Count: 5 SCOPUS Cited Count: 6
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Abstract :

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

Keyword :

distributed algorithm local information sensing capability Wireless Sensor Networks (WSNs) limited Voronoi partition

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GB/T 7714 He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range [J]. | SENSORS , 2018 , 18 (2) .
MLA He, Chenlong et al. "Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range" . | SENSORS 18 . 2 (2018) .
APA He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range . | SENSORS , 2018 , 18 (2) .
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An Improved Randomized Local Binary Features for Keypoints Recognition EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (6) | SENSORS | IF: 3.031
WoS CC Cited Count: 3 SCOPUS Cited Count: 3
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In this paper, we carry out researches on randomized local binary features. Randomized local binary features have been used in many methods like RandomForests, RandomFerns, BRIEF, ORB and AKAZE to matching keypoints. However, in those existing methods, the randomness of feature operators only reflects in sampling position. In this paper, we find the quality of the binary feature space can be greatly improved by increasing the randomness of the basic sampling operator. The key idea of our method is to use a Randomized Intensity Difference operator (we call it RID operator) as a basic sampling operator to observe image patches. The randomness of RID operators are reflected in five aspects: grids, position, aperture, weights and channels. Comparing with the traditional incompletely randomized binary features (we call them RIT features), a completely randomized sampling manner can generate higher quality binary feature space. The RID operator can be used on both gray and color images. We embed different kinds of RID operators into RandomFerns and RandomForests classifiers to test their recognition rate on both image and video datasets. The experiment results show the excellent quality of our feature method. We also propose the evaluation criteria for robustness and distinctiveness to observe the effects of randomization on binary feature space.

Keyword :

binary feature keypoints recognition ORB random ferns random forests SIFT

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GB/T 7714 Zhang, Jinming , Feng, Zuren , Zhang, Jinpeng et al. An Improved Randomized Local Binary Features for Keypoints Recognition [J]. | SENSORS , 2018 , 18 (6) .
MLA Zhang, Jinming et al. "An Improved Randomized Local Binary Features for Keypoints Recognition" . | SENSORS 18 . 6 (2018) .
APA Zhang, Jinming , Feng, Zuren , Zhang, Jinpeng , Li, Gang . An Improved Randomized Local Binary Features for Keypoints Recognition . | SENSORS , 2018 , 18 (6) .
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Object Tracking Using Local Multiple Features and a Posterior Probability Measure EI SCIE PubMed Scopus
期刊论文 | 2017 , 17 (4) | SENSORS | IF: 2.475
WoS CC Cited Count: 3 SCOPUS Cited Count: 2
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Abstract :

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.

Keyword :

multiple features object tracking centroid iteration posterior probability measure

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GB/T 7714 Guo, Wenhua , Feng, Zuren , Ren, Xiaodong . Object Tracking Using Local Multiple Features and a Posterior Probability Measure [J]. | SENSORS , 2017 , 17 (4) .
MLA Guo, Wenhua et al. "Object Tracking Using Local Multiple Features and a Posterior Probability Measure" . | SENSORS 17 . 4 (2017) .
APA Guo, Wenhua , Feng, Zuren , Ren, Xiaodong . Object Tracking Using Local Multiple Features and a Posterior Probability Measure . | SENSORS , 2017 , 17 (4) .
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Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets EI CPCI-S Scopus
会议论文 | 2017 , 4850-4855 | 29th Chinese Control And Decision Conference (CCDC)
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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For an asynchronous system based on brain-computer interface (BCI), detecting the occurrence of motor imagery by electroencephalogram (EEG) signals is the basis but also a challenge, due to the complex and non-stationary characteristics of EEG signals. This paper employs a filtering method which uses a the target guided sub-band filter combined with an energy detector for asynchronous motor imagery detection. The proposed filter in the wavelet packet transform domain uses a prior knowledge of the motor imagery and also applies the idea of background suppressing. It can pass the frequency bands that are more significant in the motor imagery signal than in the noise. Experiment demonstrated that the proposed method was reliable for practical use with an equal error rate (EER) of about 9% and a mean response time of 4.36s.

Keyword :

Motor Imagery Asynchronous Wavelet Packet Transform Brain-computer Interface

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GB/T 7714 Sun, Yujuan , Feng, Zuren , Zhang, Jun et al. Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets [C] . 2017 : 4850-4855 .
MLA Sun, Yujuan et al. "Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets" . (2017) : 4850-4855 .
APA Sun, Yujuan , Feng, Zuren , Zhang, Jun , Zhou, Qing , Luo, Jing . Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets . (2017) : 4850-4855 .
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Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification EI CPCI-S Scopus
会议论文 | 2017 , 5883-5888 | 29th Chinese Control And Decision Conference (CCDC)
WoS CC Cited Count: 12 SCOPUS Cited Count: 17
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Common spatial pattern (CSP) as a feature extraction algorithm has been successfully applied to classify EEG based motor imagery tasks in brain computer interface (BCI). Successful application of CSP depends on the character of input signals and the first and last m eigenvectors of projection matrix. In this study, we proposed a novel and robust feature extraction method designated frequency domain CSP (FDCSP) that the samples in frequency domain obtained by fast Fourier transform (FFT) algorithm and evenly distributed in 8-30Hz were employed as the input signals of CSP. Besides, we made some modifications to classical CSP to address the inconsistent issue and enhance the generalization ability. Cross validation classification accuracy and standard deviation based on training data were employed as the principle to optimize the subject-specific parameter m. Two public EEG datasets (BCI competition IV dataset 2a and 2b) were used to validate the proposed method. Experimental results demonstrated that the proposed method significantly outperformed many other state-of-the-art methods in classification performance. What's more, samples in frequency domain as the input signals of CSP are demonstrated more robust against preprocessing. Based on the two public datasets, the proposed FDCSP method has potential significance to motor imagery based BCI design in practice.

Keyword :

Motor Imagery Tasks Classification Frequency Domain Samples Common Spatial Pattern (CSP)

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GB/T 7714 Wang, Jie , Feng, Zuren , Lu, Na . Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification [C] . 2017 : 5883-5888 .
MLA Wang, Jie et al. "Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification" . (2017) : 5883-5888 .
APA Wang, Jie , Feng, Zuren , Lu, Na . Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification . (2017) : 5883-5888 .
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Community Detection Using Dual-Representation Chemical Reaction Optimization SCIE
期刊论文 | 2017 , 47 (12) , 4328-4341 | IEEE TRANSACTIONS ON CYBERNETICS | IF: 8.803
WoS CC Cited Count: 10
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Many complex networks have been shown to have community structures. Detecting those structures is very important for understanding the organization and function of networks. Because this problem is NP-hard, it is appropriate to resort to evolutionary algorithms. Chemical reaction optimization (CRO) is a novel evolutionary algorithm inspired by the interactions among molecules during chemical reactions. In this paper, we propose a CRO variant named dual-representation CRO (DCRO) to address the community detection problem. DCRO encodes a solution in two representations: one is locus-based and the other is vector-based. The former representation can ensure the validity of a solution and fits for diversification search, and the latter is convenient for intensification search. We thus design two operators for CRO based on these two representations. Their cooperation enables DCRO to achieve a good balance between exploration and exploitation. Experimental results on synthetic and real-life networks show that DCRO can find community structures close to the actual ones and is capable of achieving solutions comparable to several state-of-the-art methods.

Keyword :

community detection metaheuristic complex network evolutionary computation Chemical reaction optimization (CRO)

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GB/T 7714 Chang, Honghao , Feng, Zuren , Ren, Zhigang . Community Detection Using Dual-Representation Chemical Reaction Optimization [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2017 , 47 (12) : 4328-4341 .
MLA Chang, Honghao et al. "Community Detection Using Dual-Representation Chemical Reaction Optimization" . | IEEE TRANSACTIONS ON CYBERNETICS 47 . 12 (2017) : 4328-4341 .
APA Chang, Honghao , Feng, Zuren , Ren, Zhigang . Community Detection Using Dual-Representation Chemical Reaction Optimization . | IEEE TRANSACTIONS ON CYBERNETICS , 2017 , 47 (12) , 4328-4341 .
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