Haze Grading Using the Convolutional Neural Networks

Yin, Lirong; Wang, Lei; Huang, Weizheng; Tian, Jiawei; Liu, Shan; Yang, Bo; Zheng, Wenfeng

doi:10.3390/atmos13040522

Cited by 64 publications

(25 citation statements)

References 31 publications

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“…Asongu and Odhiambo (2019) Khan et al (2020) concluded that CO 2 emission has a positive influence on the consumption of traditional energy sources and EG. Adedoyin et al (2020), ), Liu Y. et al (2022, Yin et al (2022) also checked the impact of EG on CO 2 emission and concluded that EG and CO 2 are positively correlated in Asian countries. Anwar et al (2020) examined the effect of coal usage and EG on CO 2 in Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Globalization, institutional quality, economic growth and CO2 emission in OECD countries: An analysis with GMM and quantile regression

Fatima

Zheng

2022

Front. Environ. Sci.

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This research used the dynamic panel model and QR (Quantile Regression) to examine the effect of globalization (GB), Institutional Quality (IQ), Economic Growth (EG), Electricity Consumption (EC), and Renewable Energy (RE) consumption on Carbon dioxide (CO2) emission from 1991 to 2018 in thirty-six (OCED) countries. Panel unit root tests have been employed to examine the stationarity of the study variables; the results, which included the Harris and Tzavalis (Journal of econometrics, 1999, 91 (2), 201–226) and Levin et al. (Journal of Econometrics, 2002, 108 (1), 1–24) tests, indicate that all of the variables used are a combination of I (0) and I(I). Cointegration in the study variables has been examined using the cointegration tests devised by Westerlund, Kao, and Pedroni; the results suggest that cointegration exists in the research variables. Findings of the one-step difference GMM, One-step system GMM and two-step system GMM reveal that RE consumption, GB, and IQ negatively affect the CO2 emission in (selected OECD) countries that help to reduce CO2 excretion. In contrast, EC consumption and EG become responsible for the climatic and environmental loss. Further, this study checked the robustness by using SQR (Simultaneous quantile regression) and PQR (Panel Quantile Regression). The evaluated study findings of QR (Quantile Regression) reveal that RE consumption, GB, and IQ help to reduce the CO2 emission while EG and EC consumption becomes responsible for the climatic and environmental loss. Further recommendations are suggested based on the findings.

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Section: Introductionmentioning

confidence: 99%

Globalization, institutional quality, economic growth and CO2 emission in OECD countries: An analysis with GMM and quantile regression

Fatima

Zheng

2022

Front. Environ. Sci.

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“…Comparing the proposed traceability system with the traceability system constructed by Yang et al [37], the advantage of the proposed system is that the system solves the problems of asymmetric information, difficult sharing, easy tampering, and centralized storage in the IoT-based agricultural products traceability system of previous researchers, thus making the whole chain of agricultural product information credible and reliable [38][39][40][41][42]. Meanwhile, the proposed system also overcomes the common problems of information storage isolation, data sharing of information systems, and difficult interaction in the traceability of traditional agricultural products [43][44][45][46][47]. e present work takes the resource management problem in computer system and network as the starting point, and understands the difficulties of accurate modeling of existing heuristic methods in complex environments and poor scalability in different environments.…”

Section: Display Of Main Functions Of the Agricultural Product Tracea...mentioning

confidence: 96%

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Jiang

Lei

2022

Computational Intelligence and Neuroscience

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To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), T e t r i s ∗ , Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

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“…In each sampling period, the deep neural network makes predictions for a single data packet obtained by the sensor and sends this data packet to the controller [43][44][45][46][47][48].…”

Section: System Modelmentioning

confidence: 99%

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Zheng

2022

Computational Intelligence and Neuroscience

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At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

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Haze Grading Using the Convolutional Neural Networks

Cited by 64 publications

References 31 publications

Globalization, institutional quality, economic growth and CO2 emission in OECD countries: An analysis with GMM and quantile regression

Globalization, institutional quality, economic growth and CO2 emission in OECD countries: An analysis with GMM and quantile regression

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

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