大坝安全监测智能感知与智慧管理技术研究及应用

水库大坝安全监测是工程安全的重要保障措施,亟需结合新一代信息技术,提升大坝安全监测能力。系统总结了长江科学院近年来在大坝安全监测智能感知与智慧管理技术方面的研究及应用工作,通过研发系列化智能传感器、智能采集单元和物联网感知平台,建设统一的大坝安全监测数据资源池,开发通用化安全监测云服务系统,搭建专业数据挖掘平台和综合可视化应用,实现了大坝安全监测数据感知、传输、管理、分析及展示全链路应用,形成了大坝安全监测全生命周期智慧解决方案。研究成果已在乌东德、溪洛渡、向家坝、大藤峡等100余项水利水电工程中成功应用,为保障工程建设及运行安全发挥了重要的支撑作用,具有很好的推广应用前景。     

二次供水并联泵组节能优化研究

多泵并联变频给水作为二次供水系统中最主要的运行方式,具有稳定、可靠、节能的优点,但在实际应用中仍会因为工况分析不全面、泵组搭配不佳等问题导致节能效果不尽人意.为此,以二次供水常用的同型号双泵、三泵变频泵组为对象,探究其并联运行工况,提出"过渡低效区"的概念并分析其危害、产生原因及消除方式,借此概念给出了"改用变频大泵+工频小泵"以及"增加变频泵"的两种节能优化措施,推导了两种优化方式在双泵、三泵并联改造中消除"过渡低效区"的判定公式,并将其推广至n台水泵并联形式.最后以一工程实例模拟分析并联泵组优化措施的节能效果,结果表明各类优化方案均比原始方案更加节能,具有一定的实际意义,其中改用"变频大泵+工频小泵"的方式节能性最好.     

基于水表误差曲线重建的供水系统表观漏损量估算方法及应用

在分类用户用水模式分析的基础上,考虑选取5个特征流量进行水表误差曲线重建,对水表误差值进行修正,建立了基于水表误差曲线重建的表观漏损量估算方法.利用该方法对苏州市研究区域内新建小区、老旧小区、工业、商业、学校和医院等6类用户的水表误差进行修正,各水表的误差修正值分别为-0.25％、-4.1％、-4.59％、-2.03％、-5.74％、-0.34％,据此计算得到各类用户的表观漏损水量分别为7.41万m3、103.42万m3、292.50万m3、28.29万m3、167.83万m3、2.82万m3.与传统方法相比,建立的方法考虑到用户用水模式对水表误差曲线的影响,对表观漏损水量估算更为准确,能为用水行为类似的苏南地区的表观漏损水量估算提供参考.     

降水和人类活动对乌江上游径流变化的影响分析

径流量变化与气候和人类活动密切相关,其相互作用使得流域水循环发生变化,对气候变化和人类活动进行定量分析,可为流域调控水资源效率、协调农业发展等提供参考。以乌江上游为例,基于鸭池河水文站和流域内及周边共12个气象站,根据1960-2013年长时间序列实测径流与气象资料,利用Mann-Kendall法、累积距平法判断流域降水量和径流量的突变时间,用Morlet小波法对径流量进行周期变化分析,重点分析乌江上游流域降水与人类活动对径流量变化的贡献率。结果表明:1960-2013年间,径流量与降水量无显著增加趋势;径流量和降水量在此期间存在1986和2002年两个突变点,且突变特征和阶段特征同步;径流量与降水量的共振周期为18～19 a及19～20 a,流域径流量对降水量变化具有一定滞后作用,滞后时间为1～2 a;以1960-1986年为基准期,1987-2002年降水和人类活动对径流的贡献率分别为50.93%和49.70%,2003-2013年降水和人类活动对径流的贡献率分别为49.30%和50.70%。降水对径流变化的影响程度在降低,而人类活动对径流变化的作用在增大,因此,未来流域水资源管理应重视良性人类活动的构建与调控。     

Maximizing learning without sacrificing the fun: Stealth assessment,adaptivity and learning supports in educational games

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Layout Optimization for Greenhouse WSN Based on Path Loss Analysis

When wireless sensor networks (WSN) are deployed in the vegetable greenhouse with dynamic connectivity and interference environment, it is necessary to increase the node transmit power to ensure the communication quality, which leads to serious network interference. To offset the negative impact, the transmit power of other nodes must also be increased. The result is that the network becomes worse and worse, and node energy is wasted a lot. Taking into account the irregular connection range in the cucumber greenhouse WSN, we measured the transmission characteristics of wireless signals under the 2.4 Ghz operating frequency. For improving network layout in the greenhouse, a semi-empirical prediction model of signal loss is then studied based on the measured data. Compared with other models, the average relative error of this semi-empirical signal loss model is only 2.3%. Finally, by combining the improved network topology algorithm and tabu search, this paper studies a greenhouse WSN layout that can reduce path loss, save energy, and ensure communication quality. Given the limitation of node-degree constraint in traditional network layout algorithms, the improved algorithm applies the forwarding constraint to balance network energy consumption and constructs asymmetric network communication links. Experimental results show that this research can realize the energy consumption optimization of WSN layout in the greenhouse.     

Parallel optimization of three-dimensional wedge-shaped underwater acoustic propagation based on MPI+OpenMP hybrid programming model

The three-dimensional wedge-shaped underwater acoustic propagation model exists analytical solution, which provides verification for models like FOR3D propagation model under certain situation. However, the solving process of a three-dimensional complex underwater sound field problem is hindered by intensive computing and long calculation times. In this paper, we exploit a hybrid parallel programing model, such as MPI and OpenMP, to accelerate the computation, design various optimization methods to improve the overall performance, and then carry out the performance and optimization analysis on the Tianhe-2 platform. Experiments show that the optimized implementation of the three-dimensional wedge-shaped underwater acoustic propagation model achieves a 46.5 speedup compared to the original serial program, thereby illustrating a substantial performance improvement. We also carried out scalability tests and parallel optimization experiments for large-scale practical examples.

     

Segmentation by Continuous Latent Semantic Analysis for Multi-structure Model Fitting

International Journal of Computer Vision - In this paper, we propose a novel continuous latent semantic analysis fitting method, to efficiently and effectively estimate the parameters of model...     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...