Monthly Streamflow Forecasting Using Convolutional Neural Network

Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

     

Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation

Heuristic algorithms (HAs) are widely used in multi-objective reservoir optimal operation (MOROO) due to the rapidity of the calculation and simplicity of their design. The literature usually focuses on one or two categories of HAs and simply reviews the state of the art. To provide an overall understanding and a specific comparison of HAs in MOROO, differential evolution (DE), particle swarm optimisation (PSO), and artificial physics optimisation (APO), which serve as typical examples of the three categories of HAs, are compared in terms of the development and applications using a designed experiment. Besides, the general model with constraints and fitness function, and the solution process using a hybrid feasible domain restoration method and penalty function method are also presented. Taking a designed experiment with multiple scenarios, the mean average of the optimal objective function values, the standard deviation of optimal objective function values, the mean average of the computational time, and population diversity are used for comparisons. Results of the comparisons show that (a) the problem of optimal multipurpose reservoir long-term operation is a mathematic programming problem with narrow feasible region and monotonic objective function; (b) it is easy to obtain the same optimal objective function value, but different optimal solutions using HAs; and (c) comparisons do not result in a clear winner, but DE can be more appropriate for MOROO.

     

基于GIS的黑龙江省多年平均降水量空间插值分析比较

以黑龙江省分辨率为90m的数字高程模型(DEM)的数据为基础,结合ArcGIS软件,分析黑龙江省83个气象站点1971—2018年的降水资料,采用克里金法(Kriging)、反距离权重法(IDW)、径向基函数法(RBF)、全局多项式插值法(GPI)和趋势面法(TS)五种方法进行空间插值模拟.结果表明,黑龙江省年平均降水量东多西少,具有自边缘到中心逐渐增加的特征.西南部和西北部(漠河和北极村)的降水量大多在500mm以下,部分中部地区的降水量超过700mm.总体年平均降水量在400mm—700mm之间,大部分地区属于半湿润区.在该研究区域中,通过克里金指数模型插值方法获得结果的准确性优于其他插值方法.     

军用ASIC对策的探索与思考

本文从军用ASIC的技术基础,需求牵引和效益目标出发,论述了军用ASIC的发展模式和对策。建立军用Foundry,在通用CAD基础上实现军用ASIC的CAD工具是必要的。门阵列和标准单元是两种主要方法。从全定制向单元库过渡,是发展军用模拟ASIC和A/D ASIC的可行方案。努力发展整机-元器件垂直集成,提高ASIC的“自用率”,有利于促进军民结合。     

混合集总—分布参数微波功率分配器的机助设计

在本文中,将微波功率分配器看做用多个两端口网络构成的多端口匹配网络,用集总与分布参数元件混合组成的模块来综合这种网络,从而得到了一种新型的混合集中分布参数微波功率分配器及其机助设计方法。     

Hot corrosion ofα-SiC ceramics by V2O5 melt

The corrosion behaviour of-SiC in V₂O₅ melt has been investigated at elevated temperatures. The corrosion products on the surface of the specimen are removed using HF. The morphologies are also examined. From the observations of bubble formation in the scale and the temperature dependence of the corrosion rate, a kinetic mechanism is proposed. Based on the consistency of the plotted data with the proposed equation and high values of surface reaction rate constant, a diffusion controlling process has been developed.     

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems

Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.

     

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.     

Personalizing 3D virtual fashion stores: Exploring modularity with a typology of atmospherics based on user input

This research develops a typology of atmospherics that contains user-identified modules and modular options for personalizing 3D virtual fashion stores. A content analysis of 46 focus group discussions (n = 170) was conducted to understand the user’s perspective for personalizing 3D virtual fashion store atmospherics. Based on three atmospheric categories (pathfinding assistant, environment, and the manner of product presentation), 17 modules and 207 modular options were identified for personalizing 3D virtual stores. This research pioneers the development of an atmospherics typology for personalizing 3D virtual shopping environments as a persuasive selling tool in the emerging field of 3D virtual reality (VR) retailing.