Comparative Study on Theory Model and Test Result for Dilute sulfuric Acid to Erode Concrete

AbstractIn order to find the action mechanism for dilute sulfuric acid to erode concrete, immersion test has been carried out to concrete samples for a long time. Under the premise that PH value of immersion solution is kept constant basically, acid-consuming speed of concrete sample is determined according to titer, and according to assumption of reaction boundary layer and dynamic model of chemical reaction, the theory formula that acid-consuming speed changes with time is deduced, and the action rule for sulfuric acid to erode concrete is obtained finally: course of test sample that acid-consuming speed changes with time can be divided into two phases, respectively quick erosion and stable erosion. According to analysis of immersion test data, test result and theory model are of high degree of fitting, and theory model is correct

Weather Support for the 2008 Olympic and Paralympic Sailing Events

The Beijing 2008 Olympic and Paralympic Sailing Competitions (referred to as OPSC hereafter) were held at Qingdao during August 9–23 and September 7–13 2008, respectively. The Qingdao Meteorological Bureau was the official provider of weather support for the OPSC. Three-dimensional real-time information with high spatial-temporal resolution was obtained by the comprehensive observation system during the OPSC, which included weather radars, wind profile radars, buoys, automated weather stations, and other conventional observations. The refined forecasting system based on MM5, WRF, and statistical modules provided point-specific hourly wind forecasts for the five venues, and the severe weather monitoring and forecasting system was used in short-term forecasts and nowcasts for rainstorms, gales, and hailstones. Moreover, latest forecasting products, warnings, and weather information were communicated conveniently and timely through a synthetic, speedy, and digitalized network system to different customers. Daily weather information briefings, notice boards, websites, and community short messages were the main approaches for regatta organizers, athletes, and coaches to receive weather service products at 8:00 PM of each day and whenever new updates were available. During the period of OPSC, almost one hundred people were involved in the weather service with innovative service concept, and the weather support was found to be successful and helpful to the OPSC

Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events

Simulation Analysis of Mass Concrete Temperature Field

AbstractThe cracking phenomenon of mass concrete in the foundation mat of high-rise building is a key issue concerned by the engineering. The temperature control of mass concrete has great significance in assuring the project quality. At present, the temperature control in engineering practice is mostly limited to the temperature monitor. In this paper, MIDAS finite element analytical software was used to simulate the temperature field of mass concrete in a certain high-rise building foundation, the change rules of temperature and temperature stress with the time was calculated and analyzed by finite element analysis; at the same time, simulation result was verified by the temperature measurement data. It concludes that key to temperature control of mass concrete lies in the dual control of temperature and temperature stress. The simulation analysis of finite element procedure is feasible as an auxiliary method of temperature control and management

Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods