Modular Soft Robotic Crawlers Based on Fluidic Prestressed Composite Actuators

Soft robotic crawlers have limited payload capacity and crawling speed.This study proposes a high-performance inchworm-like modular robotic crawler based on fluidic prestressed composite(FPC)actuators.The FPC actuator is precurved and a pneumatic source is used to flatten it,requiring no energy cost to maintain the equilibrium curved shape.Pressurizing and depressurizing the actuators generate alternating stretching and bending motions of the actuators,achieving the crawling motion of the robotic crawler.Multi-modal locomotion(crawling,turning,and pipe climbing)is achieved by modular reconfiguration and gait design.An analytical kinematic model is proposed to characterize the quasi-static curvature and step size of a single-module crawler.Multiple configurations of robotic crawlers are fabricated to demonstrate the crawling ability of the proposed design.A set of systematic experiments are set up and conducted to understand how crawler responses vary as a function of FPC prestrains,input pressures,and actuation frequencies.As per the experiments,the maximum carrying load ratio(carrying load divided by robot weight)is found to be 22.32,and the highest crawling velocity is 3.02 body length(BL)per second(392 mm/s).Multi-modal capabilities are demonstrated by reconfiguring three soft crawlers,including a matrix crawler robot crawling in amphibious environments,and an inching crawler turning at an angular velocity of 2/s,as well as earthworm-like crawling robots climbing a 20 inclination slope and pipe.

Wireless Autonomous Soft Crawlers for Adjustable Climbing Actuation

Artificial soft actuators,featured with non-equilibrium internal circumstance and fast,programmable shape transformations,have attracted strong research interest recently due to their flexibility,highly controllable,and designability.However,wireless soft actuators,achieving the locomotion on different large slopes with multiple energy conversion,have been rarely reported.Herein,we create a asymmetric bilayer strategy to construct autonomous soft crawler via“breathing”moisture to motivate the mechanical deformation.The soft crawlers present conspicuous performances including periodic tumbler locomotion predicted via improved Timoshenko’s equation,multiple reversible shape-morphing(circle,helix,despiralization,etc.)determined by their fiber orientation,controlled drive mode(front drive and rear drive)and rapid climb speed(4.76 cm/min)at wide slope angles.Through architecture design,they can be series-wound or shunt-wound to construct multijoint complex actuators.Besides climbing,a intelligent soft ring-pull with admirable cycle performance for preventing overheating or something untouchable,has been proposed.The soft crawlers also realize multiple energy conversion to be actuated by light irradiation.We envision that this soft crawler system has an enormous potential in intelligent machine,microscopic diagnosis and treatment,biosensing,energy harvesting and conversion.

PathMarker:protecting web contents against inside crawlers

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).

PathMarker:protecting web contents against inside crawlers

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).

Monitoring Peer-to-Peer Botnets:Requirements,Challenges,and Future Works

The cyber-criminal compromises end-hosts(bots)to configure a network of bots(botnet).The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer(P2P)networks.The P2P botnets leverage the privileges of the decentralized nature of P2P networks.Consequently,the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures.Some P2P botnets are smarter to be stealthy in their Commandand-Control mechanisms(C2)and elude the standard discovery mechanisms.Therefore,the other side of this cyberwar is the monitor.The P2P botnet monitoring is an exacting mission because the monitoring must care about many aspects simultaneously.Some aspects pertain to the existing monitoring approaches,some pertain to the nature of P2P networks,and some to counter the botnets,i.e.,the anti-monitoring mechanisms.All these challenges should be considered in P2P botnet monitoring.To begin with,this paper provides an anatomy of P2P botnets.Thereafter,this paper exhaustively reviews the existing monitoring approaches of P2P botnets and thoroughly discusses each to reveal its advantages and disadvantages.In addition,this paper groups the monitoring approaches into three groups:passive,active,and hybrid monitoring approaches.Furthermore,this paper also discusses the functional and non-functional requirements of advanced monitoring.In conclusion,this paper ends by epitomizing the challenges of various aspects and gives future avenues for better monitoring of P2P botnets.

Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

Stress Path Analysis of Deep-Sea Sediments Under the Compression-Shear Coupling Load of Crawler Collectors

The mechanical properties of deep-sea sediments during the driving process of crawler collectors are essential factors in the design of mining systems.In this study,a crawler load is divided into a normal compression load and a horizontal shear load.Then,the internal stress state of sedimentary soil is examined through a theoretical calculation and finite element numerical simulation.Finally,the driving of crawlers is simulated by changing the relative spatial position between the load and stress unit,obtaining the stress path of the soil unit.Based on the calculation results,the effect of the horizontal shear load on the soil stress response is analyzed at different depths,and the spatial variation law of the soil stress path is examined.The results demonstrate that the horizontal shear load has a significant effect on the rotation of the principal stress,and the reverse rotation of the principal stress axis becomes obvious with the increase in the burial depth.The stress path curve of the soil is different at various depths.The spatial variation rule of the stress path of the shallow soil is complex,whereas the stress path curve of the deep soil tends to shrink as the depth increases.The stress path of the corresponding depth should be selected according to the actual research purpose and applied to the laboratory test.

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.

Analysis on steering characteristics of crawler pipeline robot

In order to improve the elbow passing performance and different diameter adaptability of pipeline robot,a supported crawler pipeline robot is designed,which adopts screw nut mechanism and hinge four-bar mechanism to adapt to the complex environment such as variable diameter pipeline and elbow.The steering characteristics passing through the elbow are studied,the kinematic of pipeline robot bending steering is established,the geometric constraint(GC)and steering constraint(SC)in the elbow are analyzed,and the steering experiment is conducted.The results show that the robot can pass through the elbow by the SC model.The SC model can reduce the motor current and energy consumption when the robot passes through the elbow.

领域相关的Web网站抓取方法

本文提出了一种抓取领域相关的Web站点的方法,可以在较小的代价下准确地收集用户所关心领域内的网站。这种方法主要改进了传统的聚焦爬虫(Focused Crawler)技术,首先利用Meta-Search技术来改进传统Crawler的通过链接分析来抓取网页的方法,而后利用启发式搜索大大降低了搜索代价,通过引入一种评价领域相关性的打分方法,达到了较好的准确率。本文详细地描述了上述算法并通过详细的实验验证了算法的效率和效果。

网络信息资源构建与维护方法研究

基于Web的信息资源组织与构建是当今IT产业面临的重大课题。设计优化的组织模型及自动构建和维护网络信息资源是一项基本建设。本文阐述了网络信息的组织原理与构建方法 ,认为利用网络机器人是组织与维护网络信息资源的有效模式 。

科技信息的网络动态监测和信息自动获取技术研究

文章综述了国内外基于Robot的Web信息检索(IR)、定题检索、智能信息检索及其代理以及相关的信息自动获取技术的有关理论、方法和技术,探讨了适合技术预测与评估的需要、用于科学领域专题信息采集的、网络科技信息智能获取的方法和策略。构建了网络科技信息动态监测和信息自动获取系统的总体框架,实现了一个基于Web的系统原型(简称BIT),并分析了该系统的特点。

深度Web资源探测系统的研究与实现

本文介绍了深度Web的资源重要性和传统爬虫工作的原理。为了能够更好的利用传统爬虫获取深度Web资源和解决传统爬虫在工作中的不足,提出了一种任务可定制化的爬虫框架,并基于可定制化的任务,实现探测网络深度资源的功能。

一种并行Crawler系统中的URL分配算法设计

研究了分布式体系结构下的并行Crawler采集模型,分析了各组件的功能及各Cmwler在并行搜索时,为保证系统的负载均衡而应遵循的基本规则,并提出了一种基于散列(hash)的URL的调度算法。

面向动态网页爬行的Crawler架构

本文分析了Crawler动态网页爬行功能,提出了面向动态网页爬行的Crawler架构,并对相应模块进行了探讨。

基于神经网络的增量式crawler重访频率研究

crawler是搜索引擎必备的核心组件 ,以何种频率对变化的Web页面进行重访是增量式crawler要解决的主要问题 .结合人工神经网络建立页面变化模型 ,由模型确定增量式crawler重访时间 ,同时分析模型在实践中的应用 ,提出一种应用方案 ,具有较好的自适应性 .

A New Framework for Focused Web Crawling

Focused crawlers are important tools to support applications such as specialized Web portals, online searching, and Web search engines. A topic driven crawler chooses the best URLs and relevant pages to pursue during Web crawling. It is difficult to deal with irrelevant pages. This paper presents a novel focused crawler framework. In our focused crawler, we propose a method to overcome some of the limitations of dealing with the irrelevant pages. We also introduce the implementation of our focused crawler and present some important metrics and an evaluation function for ranking pages relevance. The experimental result shows that our crawler can obtain more ＂important＂ pages and has a high precision and recall value.

一个自动发现确定主题下资源的系统

介绍了NDDS(NanDaDolphinSearcher)系统的设计与实现的关键技术。系统使用VSM(VectorSpaceMod el)技术来自动地确定搜索主题。智能Crawler技术能够有目标、有选择地发现新的相关资源。链接分析技术用来分析发现最重要的资源,对资源按重要性进行排序。NDDS的两种运行方式分别提供了个性化搜索服务和共享资源服务。

基于Node.JS的轻量级定向爬虫算法的设计与应用

首先介绍了网络爬虫的定义,给出其分类及工作原理,最后在介绍垂直爬虫Web magic框架的基础上,设计并实现了基于Node.JS的轻量级网络爬虫,并应用于交通应急网站新闻抓取模块,效果较好.

主题Web信息采集技术

在互联网高速发展的今天,搜索引擎逐渐成为用户在Web上获取信息的主要工具。传统的通用搜索引擎利用一个Crawler程序面向整个Web进行信息采集,它的缺点是采集无针对性、页面失效率高、不能满足特定专业人群的需要。针对这种情况,需要一个分类细致精确、数据全面深入、更新及时的面向主题的搜索引擎。