Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108kW⋅h and 306.91 × 108kW⋅h), which proved that the operation diagram constructed in this study was reasonable.

Characterization Method and Application of Heterogeneous Reservoir Based on Different Data Quantity

Deep saline aquifers have strong heterogeneity under natural conditions, which affects the migration of carbon dioxide (CO2) injection into the reservoir. How to characterize the heterogeneity of rock mass is of great significance to research the CO2 migration law during CO2 storage. A method is proposed to construct different heterogeneous models from the point of view of whether the amount of data is sufficient or not, the wholly heterogeneous model with sufficient data, the deterministic multifacies heterogeneous model which is simplified by lithofacies classification, and the random multifacies heterogeneous model which is derived from known formation based on transfer probability theory are established, respectively. Numerical simulation is carried out to study the migration law of CO2 injected into the above three heterogeneous models. The results show that the migration of CO2 in heterogeneous deep saline aquifers shows a significant fingering flow phenomenon and reflect the physical process in CO2 storage; the migration law of CO2 in the deterministic multifacies heterogeneous model is similar to that in the wholly heterogeneous model and indicates that the numerical simulation of simplifying the wholly heterogeneous structure to the lithofacies classification structure is suitable for simulating the CO2 storage process. The random multifacies heterogeneous model based on the transfer probability theory accords with the development law of sedimentary formation and can be used to evaluate the CO2 migration law in unknown heterogeneous formations. On the other hand, by comparing the dry-out effect of CO2 in different heterogeneous models, it is pointed out that the multifacies characterization method will weaken the influence due to the local homogenization of the model in small-scale research; it is necessary to refine the grid and subdivide the lithofacies of the local key area elements to eliminate the research error. The research results provide feasible references and suggestions for the heterogeneous modeling of the missing data area and the simplification of large-scale heterogeneous models.

Measurement, Distribution Characteristics and Regional Differences Analysis of Eco-efficiency Development in Tobacco Planting Zone

The positive development of ecological efficiency is of great significance for the high quality synchronization development of economy and ecological environment. In order to study the temporal and spatial distribution dynamics and regional differences of the ecological efficiency of tobacco planting zone in China, the Super-SBM model was used to calculate the ecological efficiency of provincial tobacco planting zone in China from 2005 to 2019 from economic perspective, and the kernel density function, Gini coefficient, σ convergence and Markvo transition matrix were used to analyze the dynamics of spatial distribution and regional differences in this paper. The research results show that: (1) China's overall ecological efficiency exhibits a U-shaped curve, i.e., decreased first and then increased. (2) The overall difference in eco-efficiency of tobacco planting zone is large, which is mainly originated from the inter-regional differences and hypervariable density. (3)The eco-efficiency development of tobacco planting zone in China does not have the characteristics of convergence, resulting in differences in spatial distribution. (4) The national and regional eco-efficiency of tobacco planting zone has the greatest transfer probability from year t to year t+1 in the same level of eco-efficiency.

Pedestrian Evacuation Model considering Dynamic Emotional Update in Direction Perception Domain

Traditional dynamic models cannot fully describe the microdetails of the impact of emotional contagion on individual state and behavior when pedestrian evacuation is simulated. This paper addresses the problem by constructing a dynamic evolution mechanism among emotion, state, and behavior. First, the direction perception domain of pedestrians during evacuation is defined. Then, the dynamic emotional perception of pedestrians during an evacuation is studied, considering the emotional increment caused by personal walking speed and others in the direction perception domain. Next, emotional contagion is introduced into the improved cellular automata (CA) simulation model in the floor field (FF), entitled the “CECA model.” The transfer probability of pedestrians in different states is proposed by defining “susceptible emotional state” and “infectious emotional state.” Finally, the simulation results are compared with known models. The results demonstrate that the improved model can improve the evacuation efficiency of the system significantly. Simultaneously, the effects of emotional threshold, infection coefficient, calm coefficient, and perception radius of the evacuation system on the pedestrian evacuation process are simulated and analyzed, providing a basis for evacuation managers to formulate evacuation strategies.

Research on Disability Grading Based on ICF Functional Framework: Empirical Evidence From Zhejiang Province, China

Through assignment method, the total score of disability in multiple dimensions is obtained, and it is divided into five functional states—severe disability, partial disability, moderate disability, mild disability, and health—according to the score, and the probability of death is constructed. Using the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database tracking survey data, by constructing a multistate transition probability matrix, the empirical calculation of the multistate disability transfer probability, with the help of the sixth national census data, we estimated maintenance time of each state, life expectancy, etc. The results show that the 3 year transfer probability of the initial healthy elderly is the highest, and the mortality rate is also the lowest. It can be found that the disability state transition probability measurement based on the data is more accurate than the model estimation; the disability scale and life expectancy estimated based on the multistate transition probability matrix are more reliable.

Transfer Learning and Identification Method of Cross-View Target Trajectory Utilizing HMM

The behavior identification of the target trajectory is one of the important issues in space behavior analysis. Since the target trajectory model obtained from a fixed view cannot be adapted to the change of the observation perspective, it needs to be retrained when being faced with a new view, which leads to a great amount of increment in application cost. This study proposes a hidden Markov model (HMM) based on the cross-view transfer learning and the recognition method that firstly constructs a linear mapping relationship between the observation matrices of the source and target view utilizing the domain trajectory of the HMMs and obtains the observation matrix parameters of the target domain through the mapping system. Secondly, the transfer probability of the source domain is further optimized to obtain the target domain of the HMM and to identify the behavior of the target domain trajectory utilizing a small number of samples from the view of the target domain. The experimental results denote that the proposed method could effectively realize the identification of the trajectory behavior utilizing a small sample size in the target domain and would greatly reduce the application cost of the identification of the cross-view target trajectory.

Evaluation for machine tool components importance based on improved LeaderRank

The existence of the failure transitivity of machine tool components makes the fault transfer probability of components demonstrate dynamic time-variability, which affects the importance of components and further affects the machine maintenance cycle. Therefore, studying fault transfer probability and the importance of machine tool components is necessary. In this article, the fault transfer probability of component is defined according to component fault propagation directed graph and component independent fault and related fault model based on time correlation. Assuming that the fault propagation follows the Markov process, the improved LeaderRank algorithm is applied to evaluate the importance of components by introducing background node and calculating failure impact degree of component on the basis of out-degree. Finally, the specific application is verified by taking the fault information of a certain type of machine as an example.

Students’ emotional analysis on ideological and political teaching classes based on artificial intelligence and data mining

Ideological and political teaching emotion is an important reflection of students’ learning achievements. At present, the effect of emotion analysis of ideological and political students is poor. This article builds on the artificial intelligence technology and combines machine learning data mining ideas to construct a student emotion analysis model in the ideological and political classroom. Starting from the individual, based on the individual’s own emotions and external stimuli, this article carries out emotion transfer probability statistics on the dialogues with emotions marked, and obtains the individual’s emotion transfer matrix. After the corresponding model is constructed, it can be applied to practice, and the research is conducted from the aspects of systematic emotion analysis effect and teaching promotion effect. In addition, this study designs a controlled experiment to analyze the effects of the model. The research results show that the model constructed in this paper has good performance.