面向高端装备制造CAE技术的机遇和挑战

针对高端装备制造在设计、分析、制造、服役和维护等全过程中对CAE的需求,从结构、流体以及流固耦合等3个方面概述CAE技术国内外进展及其面临的机遇和挑战,指出在发展具有能够对装备在极端服役环境下准确的结构、流体和流固耦合CAE分析技术的基础之上,需不断突破面向工程应用的多物理场耦合、跨尺度分析、多学科综合设计、产品性能评估和集成等关键技术,使CAE逐步向支持现代设计和全寿命周期管理转变.     

一种基于门限签名的区块链共识算法

针对区块链应用于物联网环境下的特点和要求,分析了目前广泛应用于联盟链的实用拜占庭容错算法（PBFT）的弊端以及目前应用于共识网络中的门限签名算法存在的普遍问题,提出改进的共识算法。首先,新共识机制将网络中的节点分组用部分节点的两两通信代替所有节点的两两通信减少通信量;其次,将组合公钥的思想引入到门限签名中,减少了通信量与计算量;最后,在节点之间引入信用分机制,优化视图切换协议。仿真结果表明,新提出的共识算法在数据吞吐量以及通信时延方面有了明显的提升,并且得到了通信量最低时的最佳分组方式。     

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.

     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable ethanol concentration around the set-point (1.0 g•L^-1), and final biomass concentration of 34.5 g-DCW•L^-1, 29.2% higher than that with a conventional PID control strategy. In the experiment, the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations, as well as a final biomass concentration that was 37.4% higher than that using the conventional strategy.     

Application of deep learning algorithms in geotechnical engineering: a short critical review

Artificial Intelligence Review - With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are...     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...