Shape Memory Alloy (SMA)‐Based Microscale Actuators with 60% Deformation Rate and 1.6 kHz Actuation Speed

Shape memory alloys (SMAs) are widely utilized as an actuation source in microscale devices, since they have a simple actuation mechanism and high‐power density. However, they have limitations in terms of strain range and actuation speed. High‐speed microscale SMA actuators are developed having diamond‐shaped frame structures with a diameter of 25 µm. These structures allow for a large elongation range compared with bulk SMA materials, with the aid of spring‐like behavior under tensile deformation. These actuators are validated in terms of their applicability as an artificial muscle in microscale by investigating their behavior under mechanical deformation and changes in thermal conditions. The shape memory effect is triggered by delivering thermal energy with a laser. The fast heating and cooling phenomenon caused by the scale effect allows high‐speed actuation up to 1600 Hz. It is expected that the proposed actuators will contribute to the development of soft robots and biomedical devices.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

An approach to integrating intelligentdiagnostics and supervision of machine tools

Arguments are presented for the necessity of integrating diagnostics and supervision in technological machines. An example of integrated diagnostics and supervision of the machine tool main drive, based on an expert system and neural network, is shown. Problems of intelligent thermal displacement supervision and questions related to practical supervision of machining centres are presented.     

ARPO-2: An expert system for legal advice on the breach of building contracts

Although Berman and Hafner [Berman 1989, pp. 928–938] presented the possibility to adapt the model of reasoning of development of an expert system for medical diagnosis to the reasoning of a judge when he/she sentences criminals does not resemble the reasoning found in the decisions of physicians, mathematicians or statisticians.When a lawyer reasons, he/she not only looks for the solution of a case; he/she simultaneously looks for the bases on which his/her reasoning can rest [Galindo 1992, pp. 363–367]. That is to say, he/she not only needs to find the solution but moreover he/she has to find the references (laws, jurisprudence and bibliography) that allow him/her to argue the solution.In many cases, computer solutions to these reasoning processes have been made in a separated way: the solution to the cases using expert systems, and the search of documentation using information retrieval systems.This paper presents the ARPO-2 prototype, a solution integrating the two aspects of legal reasoning: an expert system which is able to simultaneously find the solution to a problem and to give the necessary references so that the lawyer argues the solution. The subject on which the prototype solves problems is the breach of building contracts.In this paper, we describe the process of development of an expert system for solving, justification and documentation of breach of contracts, giving details on the way how the objects that intervene in the case were defined as well as on the reasoning followed.This paper was funded in part by DGICYT, Spanish Civil Law Computerization Project: PB870-632.     

远程勘验取证分析软件开发与实现

远程勘验分析技术是计算机取证技术的一个重要分支,而如何实现自动、智能远程勘验分析目标网站是一项重要研究课题。文章简要介绍了远程勘验取证分析软件＂网际无痕特种兵＂基本原理和功能,进行了实证性分析并取得了积极的成果。     

基于带有惩罚因子的阴性选择算法的恶意程序检测模型

提出了一个基于带有惩罚因子的阴性选择算法的恶意程序检测模型.该模型从指令频率和包含相应指令的文件频率两个角度出发,对指令进行了深入的趋向性分析,提取出了趋向于代表恶意程序的恶意程序指令库.利用这些指令,有序切分程序比特串,模型提取得到恶意程序候选特征库和合法程序类恶意程序特征库.在此基础上,文中提出了一种带有惩罚因子的阴性选择算法(negative selection algorithm with penalty factor,NSAPF),根据异体和自体的匹配情况,采用惩罚的方式,对恶意程序候选特征进行划分,组成了恶意程序检测特征库1(malware detection signature library 1,MDSL1)和恶意程序检测特征库2(MDSL2),以此作为检测可疑程序的二维参照物.综合可疑程序和MDSL1,MDSL2的匹配值,文中模型将可疑程序分类到合法程序和恶意程序.通过在阴性选择算法中引入惩罚因子C,摆脱了传统阴性选择算法中对自体和异体有害性定义的缺陷,继而关注程序代码本身的危险性,充分挖掘和调节了特征的表征性,既提高了模型的检测效果,又使模型可以满足用户对识别率和虚警率的不同要求.综合实验...     

基于晶格的人工内分泌系统模型及在多机器人系统中的应用

为了解决多机器人系统分布式控制问题,借鉴人体内分泌系统研究的理论成果,提出基于晶格的人工内分泌系统模型.该模型以环境晶格化为基础,以细胞智能化为依托,以累加激素为纽带,以靶细胞为导向,能够适应外环境的持续变化,保持内环境的相对稳定,充分体现人体内分泌系统的自组织和自修复特性.实验结果表明,无需复杂的集中控制策略,基于该...     

基于免疫的入侵检测系统中检测子集分布策略

为了提高现有的入侵检测系统的鲁棒性及对未知入侵的检测能力,根据免疫的原理、体系结构,建立了一种新的基于免疫原理的分布式入侵检测系统模型.在该模型中,检测子集如何在各个分布式节点上合理的分配将在很大程度上影响系统的性能.在分析了影响检测子集分布空间因素的基础上,从理论上研究了分布策略的优劣,给出了一种可行的将具有部分重复检测空间的检测子尽量分配到不同检测节点的方法.实验结果表明,与单纯的顺序分配相比该策略能在部分检测节点失效时系统依然维持较高的检测率.     

基于冯•诺依曼邻域结构的人工鱼群算法

提出了一种基于冯¢ 诺依曼邻域结构的人工鱼群算法. 每条人工鱼只和与自己相连的上下左右的人工鱼进行信息交换, 从而减少了计算邻域中心位置和极值位置的计算量, 有效地维持了种群的多样性, 加快了算法的运行速度. 在觅食行为中, 人工鱼通过直接移动到搜索到的较好位置, 来加快搜索速度. 在随机游动行为中, 人工鱼以小半径进行搜索, 因此算法的优化精度得到了提高. 采用动态调整人工鱼视野和步长的方法, 较好地平衡了全局搜索能力和局部搜索能力. 仿真和实例计算结果表明, 该算法具有更好的优化性能.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。