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Dynamic evolution and trend prediction of multi-scale green innovation in China

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Geography and Sustainability 2023年第3期4卷 222-231页

作者： Xiaohua Xin Lachang Lyu Yanan Zhao College of Resource Environment and Tourism Capital Normal UniversityBeijing 100048China Beijing Urban Innovation and Development Research Center Beijing 100048China

Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in *** paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during *** used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales,including regional,provincial,and ***,we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term *** findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale,with significant spatial differences of GI decreasing in each ***,the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales,exhibiting the“club convergence”effect and a tendency to transfer to higher levels of *** effect is more pronounced on a larger scale,but it is increasingly challenging to transfer to higher *** study also indicates a steady and sustained growth of GI in China,which concentrates on higher levels over *** results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.

关键词： Green innovation Spatial pattern trend prediction Multi-scale China

STUDY ON trend prediction AND VARIATION ONTHE FLOW INTO THE LONGYANGXIA RESERVOIR

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Chinese Geographical Science 2001年第1期11卷 35-41页

作者： LAN Yong-chao, KANG Er-si, MA Quan-jie, ZHANG Ji-shi (Cold and Arid Regions Environment and Engineering Research Institute, the Chinese Academy of Sciences, Lanzhou 730000, P. R. China Lanzhou Administrative Office of Hydrology and Water Resources of t Cold and Arid Regions Environment and Engineering Research Institute the Chinese Academy of Sciences Lanzhou P.R. China Lanzhou Administrative Office of Hydrology and Water Resources of the Upper Reaches of the Huanghe River Lanzhou P. R. China

The Longyangxia Gorge Key Water Control System is the first of the stairstep power sations along the Longyangxi-a-Qingtongxia river section. It has been playing an very important role in providing power, protecting flood and ice run supplying and irrigation etc. in the northwestern China. Therefore, the study on trend prediction, variation on the flow into the Longyangxia Reservoir are of the great social and economic benefits. In the medium-and-long-range runoff forecast, all kinds of regression equation are often used for predicting future hydrologic regime. However, these regression models aren’t appropriate to super long -range runoff forecast because of the restricting on weather data and so on. So a new super long-range runoff forecast model don’t depend on Reai-time weather data and called “Period correcting for residual error series GM (1, 1) model” is presented based on analyzing for the relational hydrologic data and the variation on the flow into the Longyangxia Reservoir, and the forecast model was applied successfully to predict the recent and super long -term trends of the flow into the Longyangxia Reservoir. The results indicate that the annual flow into the Longyangxia Reservoir is in the ending minimum period of the runoff history. The runoff increasing is expected in for the coming years.

关键词： flow variation trend prediction residual error series Longyangxia Reservoir

Grey GM(1,1) Model with Function-Transfer Method for Wear trend prediction and its Application

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International Journal of Plant Engineering and Management 2001年第4期6卷 203-212页

作者： LUO You xin 1 , PENG Zhu 2 , ZHANG Long ting 1 , GUO Hui xin 1 , CAI An hui 1 1Department of Mechanical Engineering, Changde Teachers University, Changde 415003, P.R. China 2 Engineering Technology Board, Changsha Cigare Department of Mechanical Engineering Changde Teachers University Engineering Technology Board Changsha Cigarettes Factory

trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

关键词： Grey GM (1,1) model fault diagnosis function transfer method trend prediction

State Change trend prediction of Aircraft Pump Source System Based on GRU Network

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State Change Trend Prediction of Aircraft Pump Source System...

Adaptive Wavelets Based on Second Generation Wavelet Transform and Their Applications to trend Analysis and prediction

第三十九届中国控制会议

作者： Jianguo Cui Yifan Cheng Xiao Cui Jinglin Wang Mingyue Yu Liying Jiang School of Automation Shenyang Aerospace University Wind Tunnel Equipment Research and Development Department AVIC Aerodynamics Research Institute Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management

Aiming at the problem that the traditional trend prediction method has a low accuracy in predicting the state change of the pump source system due to the high working intensity and bad working environment of the aircraft pump source system, this paper designs a GRU network prediction model and studies the state change trend of the aircraft pump source system. Firstly, the acquired data of aircraft pump source system are preprocessed, and the processed data are reconstructed in phase space to construct the training sample set of network parameters. The network is trained with training samples, and the optimal trend prediction model is created by constantly adjusting the number of hidden layer neurons. The optimal trend prediction model is used to obtain the trend prediction results. In order to demonstrate the effectiveness of the GRU trend prediction model, several other prediction methods have been compared with the GRU in this paper. The results show that GRU trend prediction model has higher prediction accuracy than ARMA model and traditional BP network. Compared with the LSTM prediction model which has similar prediction performance, GRU prediction model has more advantages in model training speed and better engineering application value.

关键词： Aircraft pump source system trend prediction Recurrent Neural Network Gates Recurrent Units

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International Journal of Plant Engineering and Management 2004年第3期9卷 170-176页

作者： DUANChen-dong JIANGHong-kai HEZheng-jia SchoolofMechanicalEngineering Xi＇anJiaotongUniversityXi＇an710049P.R.China SchoolofCivilEngineering Chang＇anUniversityXi＇an710061P.R.China

In order to make trend analysis and prediction to acquisition data in amechanical equipment condition monitoring system, a new method of trend feature extraction andprediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisitiondata by means of second generation wavelet transform ( SGWT), Firstly, taking the vanishing momentnumber of the predictor as a constraint, the linear predictor and updater are designed according tothe acquisition data by using symmetrical interpolating scheme. Then the trend of the data isobtained through doing SGWT decomposition , threshold processing and SGWT reconstruction. Secondly,under the constraint of the vanishing moment number of the predictor, another predictor based on theacquisition data is devised to predict the future trend of the data using a non-symmetricalinterpolating scheme, A one-step prediction algorithm is presented to predict the future evolutiontrend with historical data. The proposed method obtained a desirable effect in peak-to-peak valuetrend analysis for a machine set in an oil refinery.

关键词： second generation wavelet transform ( SCWT) predictor updater trendanalysis trend prediction

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

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Digital Communications and Networks 2024年第3期10卷 655-665页

作者： Yaguang Lin Xiaoming Wang Liang Wang Pengfei Wan School of Computer Science Shaanxi Normal UniversityXi'an710119China

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share ***,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its *** this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this ***,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is *** this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control *** optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control ***,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

关键词： Social Internet of Things Information diffusion Dynamics modeling trend prediction Optimal control

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

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Journal of Geographical Sciences 2020年第5期30卷 757-774页

作者： WANG Shaojian GAO Shuang HUANG Yongyuan SHI Chenyi Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhou 510275China College of Urban and Environmental Sciences Peking UniversityBeijing 100871China

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent *** carbon emission performance is one way to reduce carbon *** carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy *** this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission *** traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission *** results show that urban carbon emission performance in China steadily increased during the study period with some ***,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission *** spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between *** spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"***,neighborhood backgrounds play an important role in the transfer between carbon emission performance *** on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over ***,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reducti

关键词： urban carbon emission performance super-efficiency SBM model spatial Markov chain spatiotemporal patterns trend prediction China

R/S analysis of earthquake time interval

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Acta Seismologica Sinica(English Edition) 1995年第3期8卷 481-485页

作者：刘长海刘义高张军 Seismological Bureau of Anhui Province Hefei China

The R/S analysis method of time series was suggested by Hurst in 1965, then it was used tostudy the fractional Brownian motion(FBM) and the self--affinity of natural phenomena (MandeLbrot and Wallis, 1969a 3 Feder, 1988). In this paper, we use R/S analysis method to study thechsnges of Hurst exponent H of time interval sequences Of earthquakes with time variations for 5r%ions as follows: Wuqia (38'--41'N, 73'- 77 'E, M.>3' 5) I Tangshan (38'-41'N,116. 5'-- 119. 5'E, ML 2 3); Longling (23'- 26'N, 97'-- 100'E, ML > 3); Songpan (31'- 34'N,102. 5'- 105. 5'E, ML;3); China and its vicinity (20'- 50'N, 73'-129'E, M,>5), andmake an attempt to find features of anomalous variations of H values before the moderate strongearthquakes.

关键词： fractal dimension earthquake recurrence interval trend prediction

A review of research on China's carbon emission peak and its forcing mechanism

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Chinese Journal of Population,Resources and Environment 2018年第1期16卷 49-58页

作者： Feng Wang Fang Yang School of Economics and Finance Xi'an Jiaotong University Energy Policy Research Group Judge Business School University of Cambridge Economics Research Group Taiwan Research Institute & Collaborative Innovation Center for Peaceful Development of Cross-Strait Relations Xiamen University Centre for Research in Econometric Theory and Applications Taiwan University

In order to make further steps in dealing with climate change, China proposed to peak carbon dioxide emissions by about 2030 and to make best efforts for the peaking early. The carbon emission peak target(CEPT) must result in a forcing mechanism on China's economic transition. This paper, by following the logical order from "research on carbon emission history" to "carbon emission trend prediction," from "research on paths of realizing peak" to "peak restraint research," provides a general review of current status and development trend of researches on China's carbon emission and its peak value. Furthermore,this paper also reviews the basic theories and specific cases of the forcing *** on the existing achievements and development trends in this field, the following research directions that can be further expanded are put forward. First, from the perspective of long-term strategy of sustainable development, we should analyze and construct the forcing mechanism of CEPT in a reverse thinking way. Second, economic transition paths under the forcing mechanism should be systematically studied. Third, by constructing a large-scale policy evaluation model, the emission reduction performance and economic impact of a series of policy measures adopted during the transition process should be quantitatively evaluated.

关键词： Carbon emissions peak forcing mechanism climate change trend prediction