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.

     

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.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

Entity-level sentiment prediction in Danmaku video interaction

The Journal of Supercomputing - Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral...     

Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Distributed and Parallel Databases - The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form...     

Software defect prediction model based on LASSO–SVM

Neural Computing and Applications - A software defect report is a bug in the software system that developers and users submit to the software defect library during software development and...     

Effects of assembly errors on axial positioning accuracy for rotating machinery with magnetoresistance-based magnetic encoders

Microsystem Technologies - In positioning systems, magnetic scales consisting of magnetoresistive sensors are widely used in the generation of position signals for machine tools. To obtain a...     

基于云计算技术的信息安全试验系统设计与实现

在云计算领域,信息安全显得尤为重要,是一个长期的研究课题。文章利用云计算开源平台,设计一套信息安全试验系统,利用该系统,将重要信息置于云端,客户端通过虚拟机实现远程调用,可以大大提高运行的安全性和健壮性,同时也能大大提升工作效率。     

基于SaaS的矿震远程预警系统关键技术初探

现有的矿震预警系统都是以独立形式运行于各矿区,相互之间没有关联,重复投资严重,且矿震数据海量繁杂,仅依靠现场分析无法满足实时预警需求。文章基于感知矿山物联网示范工程(夹河矿),对采用云计算SaaS模式构建的矿震远程预警系统关键技术进行了相关研究:首先给出了系统整体框架,然后重点探讨了该系统所涉及的三个关键技术,包括基于SaaS的矿震预警专家分析软件架构、多源SaaS数据库构建技术以及基于MapReduce的并发数据过滤接收技术。现场应用表明,所构建的系统已基本具备全国统一远程矿震预警系统的架构,能满足多矿区远程矿震预警的基本要求。     

应用于矿井LED电子显示牌的点阵式绘图软件设计

针对矿井LED电子显示牌采用矢量图技术绘图存在的图形绘制不精确、可视性差等问题,提出一种可应用于不同型号电子显示牌的点阵式绘图软件设计方案,介绍了该软件的总体设计及BMP图像的灰度化步骤。点阵式绘图软件基于组件的设计理念,实现了所见即所得的绘图功能、文字点阵转换功能、BMP图片导入/转换功能、ICO图标导入/导出功能、模拟显示功能及对外通用接口功能。比较发现,采用点阵式绘图软件绘制的原始图形及实际显示效果均明显好于采用矢量图技术绘制的图形。