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Keywords = triplet Markov model

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25 pages, 1844 KB  
Article
Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling
by Shaoming Peng, Gang Xiong, Jing Yang, Zhen Shen, Tariku Sinshaw Tamir, Zhikun Tao, Yunjun Han and Fei-Yue Wang
Machines 2024, 12(1), 8; https://doi.org/10.3390/machines12010008 - 22 Dec 2023
Cited by 10 | Viewed by 4211
Abstract
An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. [...] Read more.
An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms. Full article
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27 pages, 625 KB  
Article
Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking
by Guanghua Zhang, Xiqian Zhang, Linghao Zeng, Shasha Dai, Mingyu Zhang and Feng Lian
Remote Sens. 2023, 15(23), 5543; https://doi.org/10.3390/rs15235543 - 28 Nov 2023
Cited by 5 | Viewed by 1587
Abstract
In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, [...] Read more.
In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples. Full article
(This article belongs to the Section Engineering Remote Sensing)
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26 pages, 9001 KB  
Article
Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network
by Yazeed Yasin Ghadi, Adnan Ahmed Rafique, Tamara al Shloul, Suliman A. Alsuhibany, Ahmad Jalal and Jeongmin Park
Remote Sens. 2022, 14(7), 1550; https://doi.org/10.3390/rs14071550 - 23 Mar 2022
Cited by 31 | Viewed by 3521
Abstract
The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks [...] Read more.
The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system. Full article
(This article belongs to the Special Issue Pattern Analysis in Remote Sensing)
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19 pages, 2754 KB  
Article
Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor
by Haoyu Li, Stéphane Derrode and Wojciech Pieczynski
Sensors 2019, 19(19), 4242; https://doi.org/10.3390/s19194242 - 29 Sep 2019
Cited by 10 | Viewed by 4041
Abstract
Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the [...] Read more.
Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithms. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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