Numerical Analysis on Temperature Rise of a Concrete Arch Dam after Sealing Based on Measured Data

The thermal boundary conditions in the construction and operation phases of a concrete arch dam are always complex. After sealing, differences between the arch dam temperature and its sealing temperature can cause compressive or tensile stresses. Based on measured temperature of an arch dam located in China, a temperature rise phenomenon (TRP) is found in the after-sealed regions of the arch dam. By mining and analyzing the temperature data of various monitoring apparatus embedded in the arch dam, higher environment temperature is considered to be the main cause for the occurrence of the TRP. Mathematical methods for complex thermal boundary conditions, including external boundary conditions and internal heat source conditions, are proposed in this paper. A finite element model is implemented with the concern of the construction phase and operation phase of the arch dam. Results confirm good agreement with the measured temperature and verify the conjecture that the TRP occurs mainly because the external temperature of the arch dam is higher than its sealing temperature

A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation.National Key Research Development Program of China (2016YFA0601502 and 2016YFC0502800), and the 111 Project (B14008

Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search

This paper investigates the nested Monte Carlo tree search (NMCTS) for feature selection on regression tasks. NMCTS starts out with an empty subset and uses search results of lower nesting level simulation. Level 0 is based on random moves until the path reaches the leaf node. In order to accomplish feature selection on the regression task, the Gamma test is introduced to play the role of the reward function at the end of the simulation. The concept Vratio of the Gamma test is also combined with the original UCT-tuned1 and the design of stopping conditions in the selection and simulation phases. The proposed GNMCTS method was tested on seven numeric datasets and compared with six other feature selection methods. It shows better performance than the vanilla MCTS framework and maintains the relevant information in the original feature space. The experimental results demonstrate that GNMCTS is a robust and effective tool for feature selection. It can accomplish the task well in a reasonable computation budget

Selection of continuous features based on distribution of objects

A novel feature selection approach is proposed for data space defined over continuous features, which obtains a subset of features,such that it can discriminate class labels of objects and its discriminant ability is not inferior to that of the original features,so to effectively improve the learning performance and intelligibility of the classification model.According to the spatial distribution of objects and their classification labels,a data space with continuous features is partitioned into subspaces,each with a clear edge and a single classification label.Then these labelled subspaces are projected to each continuous feature.The measurement of each feature is estimated for a subspace against all other subspace-projected features by means of statistical significance.Through the construction of a matrix of the measurements of the subspaces by all features,the subspace-projected features are ranked in a descending order based on the discriminant ability of each feature in the matrix.After evaluating a gain function of the discriminant ability defined by the best-so-far feature subset,the resulting feature subset can be incrementally determined. Our comprehensive experiments on the UCI Repository data sets have demonstrated the effectiveness and efficiency of the proposed approach of feature selection

Ecological network analysis for urban metabolism and carbon emissions based on input-output tables: A case study of Guangdong province

Global warming has received more and more attention in recent years for its inevitable influence on population, species, soil, ocean, water and so on. It is essential to investigate the urban metabolism of carbon emissions which is a main cause of global warming and most of it occurs in the process of production and living in urban areas. In this paper, a carbon emission metabolic network is established to explore the emission reduction strategies by modeling carbon dioxide flows and identifying the mutual relationships based on the input-output analysis. Specifically, Eff-Lorenz curve derived from the painting of Lorenz curve is developed to compare the efficiency of carbon emissions from different sectors. The newly developed method has been applied to Guangdong province to demonstrate its availability and benefit. It is revealed that carbon emissions mainly concentrated in the secondary and tertiary industries with electric power generation, manufacturing industry, domestic consumption and transportation ranking at the top. The competition relationship reveals good interactions in terms of emission reduction while a mutualism relationship provides effective pathways to mitigate carbon emissions between pairwise sectors simultaneously. In Guangdong province, upgrading the clean combustion technology in electric power generation and energy extraction sectors would drive other sectors to cut emissions and adjusting the production structure of the construction sector also contribute to achieve this goal. The results are expected to provide corresponding and holistic reference for decision makers to develop the mitigation policies

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. In this study, a two-stage stochastic fractional programming (TSFP) method is developed for planning an agricultural water resources management system under uncertainty. TSFP can provide an effective linkage between conflicting economic benefits and the associated penalties; it can also balance conflicting objectives and maximize the system marginal benefit with per unit of input under uncertainty. The developed TSFP method is applied to a real case of agricultural water resources management of the Zhangweinan River Basin China, which is one of the main food and cotton producing regions in north China and faces serious water shortage. The results demonstrate that the TSFP model is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors. Results also indicate that, under the optimized irrigation target, the optimized water allocation rate of Minyou Channel and Zhangnan Channel are 57.3% and 42.7%, respectively, which adapts the changes in the actual agricultural water resources management problem. Compared with the inexact two-stage water management (ITSP) method, TSFP could more effectively address the sustainable water management problem, provide more information regarding tradeoffs between multiple input factors and system benefits, and help the water managers maintain sustainable water resources development of the Zhangweinan River Basin

Ensemble Transductive Propagation Network for Semi-Supervised Few-Shot Learning

Few-shot learning aims to solve the difficulty in obtaining training samples, leading to high variance, high bias, and over-fitting. Recently, graph-based transductive few-shot learning approaches supplement the deficiency of label information via unlabeled data to make a joint prediction, which has become a new research hotspot. Therefore, in this paper, we propose a novel ensemble semi-supervised few-shot learning strategy via transductive network and Dempster–Shafer (D-S) evidence fusion, named ensemble transductive propagation networks (ETPN). First, we present homogeneity and heterogeneity ensemble transductive propagation networks to better use the unlabeled data, which introduce a preset weight coefficient and provide the process of iterative inferences during transductive propagation learning. Then, we combine the information entropy to improve the D-S evidence fusion method, which improves the stability of multi-model results fusion from the pre-processing of the evidence source. Third, we combine the L2 norm to improve an ensemble pruning approach to select individual learners with higher accuracy to participate in the integration of the few-shot model results. Moreover, interference sets are introduced to semi-supervised training to improve the anti-disturbance ability of the mode. Eventually, experiments indicate that the proposed approaches outperform the state-of-the-art few-shot model. The best accuracy of ETPN increases by 0.3% and 0.28% in the 5-way 5-shot, and by 3.43% and 7.6% in the 5-way 1-shot on miniImagNet and tieredImageNet, respectively

Pressure Drop Optimization of the Main Steam and Reheat Steam System of a 1000 MW Secondary Reheat Unit

The pressure drop of a main steam and reheat steam system should be optimized during the design and operation of a thermal power plant to minimize operation costs. In this study, the pressure drop of the main steam pipe and reheat steam pipe of a 1000 MW secondary reheat unit are optimized by modulating the operation parameters and the cost of operation is explored. Optimal pipe specifications were achieved by selecting a bend pipe and optimizing the pipe specifications. The pressure loss of the main steam pipeline was optimized to 2.61% compared with the conventional pressure drop (5%), the heat consumption of steam turbine was reduced by about 0.63 kJ/(kW·h), the standard coal consumption was minimized by about 0.024 g/(kW·h), and the total income in 20 years is approximated to be CNY 217,700. The primary reheat system was optimized to 4.88%, the steam turbine heat consumption was reduced by about 7.13 kJ/(kW·h), the standard coal consumption decreased by about 0.276 g/(kW·h), and the total income in 20 years is projected to be CNY 20.872 million after the optimization of the pressure drop. The secondary reheat system was optimized to 8.13%, the steam turbine heat consumption was reduced by about 7.86 kJ/(kW·h), the standard coal consumption decreased by about 0.304 g/(kW·h), and the total income in 20 years is projected to be CNY 22.7232 million after the optimization of the pressure drop. The research results of the present study provide a guide for the design and operation of secondary reheat units to achieve an effective operation and minimize costs