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...     

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Neural Computing and Applications - Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D...     

航空发动机变结构全局快速Terminal滑动模态控制方法研究

针对航空发动机这个具有时变不确定性的非线性系统,提出了一种新型变结构全局快速最终滑动模态控制(Variable Structure Global Fast Terminal Sliding Mode Control)的航空发动机控制方法;通过对利用VSGFTSMC理论设计航空发动机最终滑动模态控制器的方法进行了深入研究,设计了航空发动机变结构全局快速滑动模态控制器;仿真结果表明,所设计的控制器的控制效果良好,对外界干扰有很强的抑制能力,使被控系统在整个控制阶段都具有较强的鲁棒性.     

基于SoC的嵌入式DRAM存储器内建自测试设计

内建自测试(Built-in Self Test,BIST)是测试片上系统(System on- Chip,SoC)中嵌入式存储器的重要技术;但是,利用BIST技术采用多种算法对嵌入式存储器进行测试仍面临诸多挑战;对此,提出了一种基于SoC的可以带有多种测试算法的嵌入式DRAM存储器BIST设计,所设计的测试电路可以复用状态机的状态,利用循环移位寄存器(Cyclic Shift Register,CSR)产生操作命令,利用地址产生电路产生所需地址;通过对3种BIST电路支持的算法,全速测试,面积开销3个方面的比较,表明提出的嵌入式DRAM存储器BIST设计在测试时间,测试故障覆盖率和测试面积开销等各方面都取得了较好的性能.     

机载塔康设备综合检查仪设计

使用市场上成熟的模块和技术研制机载塔康设备综合检查仪,以降低成本和提高设备的可靠性;根据设备的测试需求,选用嵌入式工业平板电脑、USB-ARINC429通讯卡、USB-数字IO卡、串行接口数字表和自研的塔康信号模拟器等构成检查仪硬件,使用CVI9.0编写了测试程序;应用表明:检查仪集成度高,使用方便,能够完成对塔康设备的自动及手动测试。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Enhancing collaborative filtering by user interest expansion via personalized ranking

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.