Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Self-Learning of Multivariate Time Series Using Perceptually Important Points

In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.

Where Have Network-based Self-learning Classes Gone?——Reflections & Expectations on the Employment of Network-based Self-learning Classes

To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.

SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS

This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.

Mathematical model for cooling process and its self-learning applied in hot rolling mill

Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.

Study on intelligent digital welding machine with a self-learning function

A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.

Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

Neuron self-learning PSD control for backside width of weld pool in pulsed GTAW with wire filler

In this paper, the weld pool shape control by intelligent strategy was studied. A neuron self-learning PSD controller for backside width of weld pool in pulsed GTAW with wire filler was designed. The PSD control arithmetic was analyzed, simulating experiment by MATLAB software was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were successfully implemented. The study results show that the neuron self-learning PSD control method can attain a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

The Self-Learning Gate for Quantum Computing

Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.

A Self-Learning Diagnosis Algorithm Based on Data Clustering

The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.

基于零信任机制的工业互联网边界防护方案研究

随着互联网和信息技术的快速发展,传统的工业制造与新兴信息技术、互联网技术开始互相融合,“工业互联网”逐渐崭露头角,并广泛应用于能源、电力、交通、军工、航空航天、医疗等关系到国家安全、国计民生的重要行业。工业互联网涉及到众多国家关键基础设施,因此工业互联网的安全将影响到社会安全、公众安全甚至国家安全。该文对工业互联网中存在的网络安全风险进行分析,并提出一种基于“零信任”机制的边界防护方案,在兼容数量庞大、种类繁多的工业设备、操作系统以及生产应用的同时,为整个生产内网提供整体安全防护能力。基于零信任机制的工业互联网边界防护方案区别于传统防护思路,以白名单机制代替黑名单机制,以应用隐身代替技术对抗,以动态验证代替静态检测。最后,给出了基于零信任机制实现的工业互联网边界防护应用案例,并结合系统功能分析了该方案的技术优势。

基于Android内核驱动的白名单网络控制

Android系统是目前主流的移动终端操作系统之一,其数据泄露问题日益受到学术界的广泛关注.恶意应用窃取用户敏感数据后通过互联网发送扩散,从而对用户实施进一步侵害.Android系统中网络权限属于常规权限,应用无需用户授权即可联网发送数据.针对上述问题,本文提出了一种基于Android内核驱动程序的网络白名单网络控制方案,用户可以监控所有应用程序的网络使用状态,选择信任的应用加入白名单中,对白名单中的应用程序实行内核级签名验证,防止程序代码被非法篡改,从而构建安全可控的网络使用环境.本方案为应用和内核的通信构建了专用通道,以确保网络白名单管理权限不会被其他应用窃取,随后通过进程识别针对性地管控网络权限,在不影响正常应用功能的情况下实现权限管理.经过实验验证,本方案可以有效防止恶意应用利用互联网泄露用户隐私,网络管控成功率达到了100%.系统运行稳定,被管控应用启动时间最大增加33.1%,最小增加3.6%.

Physical neural networks with self-learning capabilities

Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially surpassing the constraints of conventional digital neural networks.A recent advancement known as“physical self-learning”aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the fundamental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.

贵州习酒股份有限公司工业控制安全项目

贵州习酒积极推进白酒传统生产方式的机械化升级,推动了整个行业向信息化、智能化转型,其制造过程中的网络安全防护显得越来越重要。本方案构建了贵州习酒工控网络“垂直分层、水平分区、边界控制、内部监测”的工控安全技术防护体系,实现了对各操作站、工业控制系统连接处、无线网络等进行边界防护和准入控制,以及对工业控制系统内部进行网络流量数据监测。本方案构建的工控网络实战化主动防御体系,提升了工控网络未知威胁检测能力,实现了从被动防御变为主动防御,全面提高了工控网络威胁防御能力,全面快速消除了工控网络威胁,实现了从单点防御到工控网络全网协防,形成了贵州习酒工控网络实战化网络安全防护体系。

The Impact of Personalized Learning Path Design on Online Education Platforms on Students’Self-Learning Abilities

The study investigates the impact of personalized learning path design on students’self-learning abilities(SLA)within online education platforms.Employing a mixed-methods approach,the research examines the effectiveness of personalized learning through quantitative surveys and qualitative interviews with a diverse sample of online learners.The findings indicate that personalized learning path design significantly enhances students’self-efficacy,engagement,and satisfaction,leading to improved SLA.The study’s conceptual model and empirical data support the hypothesis that personalization in learning environments fosters self-directed learning skills.The discussion highlights the implications for educational practice,emphasizing the need for online platforms to prioritize personalization and for educators to adapt their teaching methods to support diverse learner needs.The research also acknowledges limitations and suggests future directions,including longitudinal studies and expanded participant demographics.The study concludes that personalized learning path design is a promising strategy for online education platforms to empower learners and promote lifelong learning skills.

A novel self-learning approach to overcome incompatibility on TripAdvisor reviews

Among social media networks,TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations.Sentiment analysis is amethod that can be used to analyze people's behaviors and opinions onpublic and socialmedia platforms.In this study,hotel reviews are extracted fromthe five most attractive Sri Lankan cities,and user-written reviews are compared over user bubble ratings,which define overall travelers'experiences as a numerical scale that ranks from 1 to 5.We find that the compatibility between userwritten reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users'genuine opinions expressed in their reviews.To address this problem,a two-phase approach is proposed:(1)the ensemblemethod to improve the performance of lexicon-based outputs and identify the correctlymatching user review and bubble rating;(2)the self-learning approach to finding the sentiment of a review that does not properly label by the user.The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposedmodel.For example,regardless of bigram“not good”,the average percentages of the word“good”for each negatively identified review from the proposed model and bubble rating are 25.63%and 38.85%,respectively.Thereby,it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.

可编程逻辑控制器的控制逻辑注入攻击入侵检测方法

可编程逻辑控制器(PLC)的控制逻辑注入攻击通过篡改控制程序操纵物理过程,从而达到影响控制过程或破坏物理设施的目的。针对PLC控制逻辑注入攻击,提出了一种基于白名单规则自动化生成的入侵检测方法PLCShield(Programmable Logic Controller Shield)。所提方法以PLC控制程序承载着全面、完整的物理过程控制信息为依据,主要包括两个阶段:首先,通过分析PLC程序的配置文件、指令功能、变量属性和执行路径等信息,提取程序属性、地址、值域和结构等检测规则;其次,采用主动请求PLC的运行“快照”和被动监听网络流量结合的方式,实时获取PLC当前的运行状态和流量中的操作、状态等信息,并通过对比得到的信息与检测规则识别攻击行为。以4款不同厂商和型号的PLC作为研究案例验证PLCShield的可行性,实验结果表明所提方法的攻击检测准确度达到97.71%以上,验证了所提方法的有效性。

遥感监测在智慧地铁中的应用

城市轨道交通保护区内的工厂施工、基坑开挖、钻探挖井等活动,对地铁结构造成极大影响。以北京地铁为例,通过研究遥感监测在智慧地铁中的应用,以提高城市轨道交通保护区的违规施工监测效率、降低监测成本。充分发挥融合卷积神经网络、语义分割2种方法的各自优势,利用卷积神经网络识别建筑物,再通过语义分割提取建筑物对象轮廓,以提升建筑物提取精度。根据建筑信息识别结果、遥感影像监测结果,结合北京市地铁运营有限公司提供数据,核查北京城市轨道交通保护区周边违规施工行为,将结果整理汇总,并建立北京城市轨道交通保护区白名单数据库。

基于数字化能力的目标号码短信精准治理

近年来,全国通信网涉诈短信引发用户业务使用感知持续下降,投诉工单量逐年上升,每年造成用户财产损失金额达上百亿元,极大危害群众生命财产安全,对不良短信的治理是一个与不法分子博弈的过程。随着打击力度持续加大,不法分子在短信批量发送的过程中也开始通过不停地试错、调整发送号码等手段,寻找存在治理漏洞号码类型,并灵活切换以方便其持续从事不法行为。故针对目标类型发送号码的实时监测及精准治理变得越来越必要且迫切。