Health and Safety Situation Awareness Model and Emergency Management Based on Multi-Sensor Signal Fusion

Gu, Qinghua; Jiang, Song; Lian, Minjie; Lu, Caiwu

doi:10.1109/access.2018.2886061

Cited by 33 publications

(14 citation statements)

References 38 publications

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“…Gu et al [5] presented an outline for a situation awareness system to guarantee the accuracy of signal transmission and protect workers in mines. Multi-sensor data were preprocessed, and information entropy theory was used to weight the data based on various characteristics.…”

Section: A Iot or Iomt And Medical Signalsmentioning

confidence: 99%

“…In order to ensure patients' and stakeholders' satisfaction, intelligent health care has been revolutionized with the development of AI and machine learning (ML) algorithms in the context of deep learning (DL) and wireless local area network (wLAN) technologies [5]. The medical industry has been able to manage numerous medical signals from the same user-simultaneously improving disease detection and prediction precision-due to these technologies' high computational performance, high data volume, accommodation of several terminal units, and the introduction of 5G and beyond 5G wireless technology.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

2021

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Section: A Iot or Iomt And Medical Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

2021

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“…(4) In the future, we are planning to investigate more efficient population partition mechanism to adapt to different nonseparable problems in the process of crossover operation. We are also interested in applying HGGWA to real-world problems to ascertain its true potential as a valuable optimization technique for large-scale optimization such as the setup of large-scale multilayer sensor networks [73] in the Internet of Things.…”

Section: Complexitymentioning

confidence: 99%

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Jiang

2019

Complexity

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Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.

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“…Rules. e CMSS model is implemented into the grid world based on the Netlogo simulation platform [35][36][37]. e movement of the model agent in the grid follows certain rules, as shown in Figure 4:…”

Section: Cmss Model Agent Interactivementioning

confidence: 99%

Simulation Research on the Effectiveness of a Multiagent Mine Safety Supervision System and Its Verification

Nie

Bai

Zhang

2019

Mathematical Problems in Engineering

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A Computable Mine Safety Supervision (CMSS) model is constructed based on agent-based modeling and simulation (ABMS) technology and the conservation of resources (COR). This model aims to solve the mining safety problems involved with illegal mining operations and burnout among mining supervisors, in China. The model includes several types of agents: supervision agents, decision support agents, functional coordination agents, and miner agents, and it uses the Netlogo simulation platform to simulate the influence of reward and punishment on agent behavior. The simulation determines the decision support degree to gauge the influence of functional coordination and miner behavior on the burnout rate of supervision agents. We analyze the macroscopic emergence law of the simulation results. The results show the following: (1) Job Situation Adaptability (JSA) ∈ [−6.02, 2.64] ∪ [16.9, 21.93], which uses a reward strategy to guide miners to choose safe behavior and (2) JSA ∈ [2.64, 16.9], which uses a punishment strategy to restrict unsafe behavior. The decision support coefficient Sc has the greatest influence on the supervision agent’s job burnout. The functional coordination coefficient Fc has the second highest influence on job burnout and the processing effectiveness coefficient Ec has the least influence. According to the simulation results, suggestions for improving the mine safety supervision system are put forward and an improved safety management decision-making basis for reducing mine accidents is provided.

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Health and Safety Situation Awareness Model and Emergency Management Based on Multi-Sensor Signal Fusion

Cited by 33 publications

References 38 publications

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

Hybrid Genetic Grey Wolf Algorithm for Large‐Scale Global Optimization

Simulation Research on the Effectiveness of a Multiagent Mine Safety Supervision System and Its Verification

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