Human Detection Based on Radar Sensor Network in Natural Disaster

Wang, Wei

doi:10.1007/978-981-13-0992-2_8

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Besides, it also implied that the CNN-based method was better than the DBN-based method in the processing of the complex background and noise in the A-scan data. In the work of Wang [87], a stacked denoising autoencoder was adopted to extract the high-level representations under imbalanced sample conditions by a layer-by-layer greedy training method. The outputs from the first and second hidden layers of the autoencoder can be considered as the middle-and high-level representations.…”

Section: Architectures Exploiting A-scan Datamentioning

confidence: 99%

Advances of deep learning applications in ground-penetrating radar: A survey

Zheng

Gao

Yuan

2020

Construction and Building Materials

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Section: Architectures Exploiting A-scan Datamentioning

confidence: 99%

Advances of deep learning applications in ground-penetrating radar: A survey

Zheng

Gao

Yuan

2020

Construction and Building Materials

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“…In similar application domains, e.g. detecting human bodies trapped under debris, also genetic fuzzy systems are being proposed to optimise the accuracy of the classification outcome [13], as well as neural systems and other Machine Learning (ML) methods to extract relevant features leading to a very robust and accurate final decision [14]- [18]. In this light, the study in [6] has shown that DM in environments with high levels of uncertainty and disturbances, is indeed possible with acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Robust Decision-Making Framework Based on Collaborative Agents

et al. 2020

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Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world domains without being significantly affected by undesired disturbances. Our paper presents a multi-agent based structure in which agents are capable of individual decision-making but also interact to perform subsequent, and more robust, collaborative decisionmaking processes. The complexity of each software agent can be kept quite low without a deterioration of the performance since an intelligent and robust-to-uncertainty decision-making behaviour arises when their locally produced measure of support are shared and exploited collaboratively. We show that by equipping agents with classic computational intelligence techniques, to extract features and generate measure of supports, complex hybrid multi-agent software structures capable of handling uncertainty can be easily designed. The resulting multi-agent systems generated with this approach are based on a two-phases decision-making methodology which first runs parallel local decision making processes to then aggregate the corresponding outputs to improve upon the accuracy of the system. To highlight the potential of this approach, we provided multiple implementations of the general framework and compared them over four different application scenarios. Results are promising and show that having a second collaborative decisionmaking process is always beneficial.

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