On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control

Luo, Rui; Peng, Zhinan; Hu, Jiangping

doi:10.3390/math11040906

Cited by 31 publications

(21 citation statements)

References 72 publications

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“…However, vehicle tracking in multi-camera systems is very challenging, because the tracking process must ensure the integration of information from different sensors. The system designed to deal with MOMCT tasks usually consists of five sub-modules, namely, object detection [12], multi-object single-camera tracking [13], vehicle re-identification (re-ID) [14], and multi-object multi-camera tracking [15,16]. The general process can be summarized as follows: Firstly, the vehicle detection module outputs vehicle coordinates and categories in units of frames.…”

Section: Introductionmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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Section: Introductionmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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“…In [32], a homogenization meshless collocation method is proposed to solve inverse problems in strong form particularly. The paper [33] reviews recent progress in model identification‐based learning and optimal control and its applications to multi‐agent systems. By the same approach, the authors [34] proposed a novel nonfragile memory‐based sampled‐data control approach for the consensus problem of nonlinear multiagent systems with time‐varying communication delays.…”

Section: Introductionmentioning

confidence: 99%

The inverse problem of dipolar source identification from incomplete boundary measurements

Mahjoub,

Mdimagh

2023

Math Methods in App Sciences

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We consider and combine two methods to solve the inverse problem of locating dipolar sources in a restricted domain from measurements of the Cauchy data on a part of the boundary for the Laplace equation. We begin by extending the boundary measurements, which are accessible only on a part of the boundary by solving an extremal bounded problem in an annulus. The inverse problem of source identification is then solved using the reciprocity gap concept applied to the completed data. In order to evaluate the effectiveness of source detection with regard to the expansion of the data, some numerical tests are provided.

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“…19 The real specimen data cannot usually meet the homogeneous property, due to the GM(1, 1) model being a homogeneous kind model, thus extremely restricts its feasibility. The NGM(1, 1, k ) model by Chen and Chen 32 was introduced to correct these shortcomings. Nevertheless, this model introduces some novel methods.…”

Section: Introductionmentioning

confidence: 99%

“…Substituting equation (31) into equation (32), the closedloop uncertain fuzzy relaxed system F R ðC; 0Þ is obtained as follows…”

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confidence: 99%

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Meng

Jiang

et al. 2023

International Journal of Advanced Robotic Systems

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Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel.

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On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control

Cited by 31 publications

References 72 publications

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

The inverse problem of dipolar source identification from incomplete boundary measurements

Neural ordinary differential gray algorithm to forecasting models of controlled systems

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