Evaluation on algorithm reliability and efficiency for an image flame detection technology

Yang, Yi; Wang, Xuefeng; Pan, Mengyi; Li, Pu; Tsai, Yun‐Ting

doi:10.1007/s10973-023-12012-8

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…In this scenario, without the intervention of policy and equipment transformation, it was supposed that the annual output would increase with the growth of the secondary industry GDP [21,25,[47][48][49]. Through the correlation analysis GDP of the secondary industry and the production of the three industries in recent years, a strong linear relationship has been observed among them (shown in Figures S2-S5).…”

Section: Business-as-usual (Bau) Scenariomentioning

confidence: 95%

Clearing the Air: Assessing the Effectiveness of Emission Policy in Qinhuangdao’s Key Industries

Shan,

Chen,

Wang

et al. 2023

Atmosphere

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China has successively put forward ultra-low emission (ULE) transformation plans to reduce the air pollutant emissions of industrial pollutants since 2014. To assess the benefits of the ULE policy on regional air quality for Qinhuangdao, this study developed an emission inventory of nine atmospheric pollutants in 2016 and evaluated the effectiveness of the emission policy in Qinhuangdao’s key industries under different scenarios with an air quality model (CALPUFF). The emissions of air pollutants in 2016 were as follows: Sulfur dioxide (SO2) emitted 48.91 kt/year, nitrogen oxide (NOx) emitted 86.83 kt/year, volatile organic compounds (VOCs) emitted 52.69 kt/year, particulate matter (PM10 and PM2.5) emitted 302.01 and 116.85 kt/year, carbon monoxide (CO) emitted 1208.80 kt/year, ammonia (NH3) emitted 62.87 kt/year, black carbon (BC) emitted 3.79 kt/year, and organic carbon (OC) emitted 2.72 kt/year, respectively. The results showed that at the regional level in 2025, the iron and steel industry under the PPC (Peak Production Capacity) scenario had the highest potential for reducing SO2 and NOx emissions, while the cement industry under the PPC scenario excelled in reducing PM10 emissions. As for the industrial level in 2025, the flat glass industry under the ULE scenario would reduce the most SO2 emitted, while the iron and steel industry and the cement industry under the PPC scenario demonstrated the best reduction in NOx and PM10 emissions, respectively. Furthermore, the average annual contribution concentration of SO2, NOx, and PM10 in the air monitoring stations of Qinhuangdao under the PPC scenario was significantly lower than that under the BAU scenario revealed by air quality simulation. It can be concluded that the emission policy in Qinhuangdao will help improve the air quality. This study can provide scientific support for policymakers to implement the ULE policy in industrial undeveloped cities and tourist cities such as Qinhuangdao in the future.

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Section: Business-as-usual (Bau) Scenariomentioning

confidence: 95%

Clearing the Air: Assessing the Effectiveness of Emission Policy in Qinhuangdao’s Key Industries

Shan,

Chen,

Wang

et al. 2023

Atmosphere

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“…The problem of accurately predicting the radiological hazards of a nuclear weapon explosion has been an enduring topic in the fields of atmospheric science, environmental science, and nuclear physics. A great deal of effort has been spent in previous work to study the rise of nuclear smoke clouds, particle size dispersion and other problems, and remarkable contributions have been made [49][50][51][52]. However, no model can achieve absolutely accurate predictions, and the errors introduced by the source term parameters as the input value of any model is one of the important factors affecting the prediction accuracy of the model.…”

Section: Contributions and Limitationsmentioning

confidence: 99%

Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model

Zheng¹,

Wang

et al. 2023

Atmosphere

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Many well-established models exist for predicting the dispersion of radioactive particles that will be generated in the surrounding environment after a nuclear weapon explosion. However, without exception, almost all models rely on accurate source term parameters, such as DELFIC, DNAF-1, and so on. Unlike nuclear experiments, accurate source term parameters are often not available once a nuclear weapon is used in a real nuclear strike. To address the problems of unclear source term parameters and meteorological conditions during nuclear weapon explosions and the complexity of the identification process, this article proposes a nuclear weapon source term parameter identification method based on a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) by combining real-time monitoring data. The results show that both the PSO and the GA are able to identify the source term parameters satisfactorily after optimization, and the prediction accuracy of their main source term parameters is above 98%. When the maximum number of iterations and population size of the PSO and GA were the same, the running time and optimization accuracy of the PSO were better than those of the GA. This study enriches the theory and method of radioactive particle dispersion prediction after a nuclear weapon explosion and is of great significance to the study of environmental radioactive particles.

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Vision Based Flame Detection Using Compressed Domain Motion Prediction and Multi-Feature Fusion

Tang,

Chen,

Wang

et al. 2023

Lecture Notes in Computer Science

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Evaluation on algorithm reliability and efficiency for an image flame detection technology

Cited by 8 publications

References 29 publications

Clearing the Air: Assessing the Effectiveness of Emission Policy in Qinhuangdao’s Key Industries

Clearing the Air: Assessing the Effectiveness of Emission Policy in Qinhuangdao’s Key Industries

Identification Method of Source Term Parameters of Nuclear Explosion Based on GA and PSO for Lagrange-Gaussian Puff Model

Vision Based Flame Detection Using Compressed Domain Motion Prediction and Multi-Feature Fusion

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