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 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.

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.

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.

民航员工安全信息自愿报告意愿的促进路径:基于模糊集定性比较分析

为探究民航员工安全信息自愿报告意愿的作用路径,基于个人-环境匹配理论,构建个体特征和组织环境影响员工自愿报告意愿的多条件联动模型,基于178份调查问卷,应用模糊集定性比较分析(fsQCA)方法开展验证。结果表明:安全信息自愿报告意愿的产生不是认知水平、工作嵌入、职业身份认同、领导支持和安全文化等单一条件促发的结果,而是由多个条件共同作用,高水平安全信息自愿报告意愿的促进模式有3个,分别为:员工自主驱动型,核心条件是员工的高认知水平、高工作嵌入和高职业身份认同;领导员工共鸣型,核心条件是员工的高认知水平、高职业身份认同、工作嵌入不足和组织的高领导支持;领导支持牵引型,核心条件是组织的高领导支持和安全文化缺失。

走私普通货物罪单位自首问题研究——以北京某公司走私普通货物案为例

自动投案和如实供述是自首的两个必备条件。在虚假申报类单位走私普通货物案中的自首认定过程中,主要对自动投案存在一定争议。单位犯罪的二元关系模型,对于准确走私犯罪中的单位自首和个人自首具有重要意义。应从单位走私犯罪中单位与自然人的二元定罪量刑模式、单位走私犯罪中单位和个人自动投案的分离认定、单位走私犯罪中主管人员自首推定单位自首三个方面,分析走私普通货物罪单位自首的若干疑难问题,从而准确认定自首,做到罪责刑相适应。

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.

小鼠运动方式——转笼的制作与应用

旨在研制能记录小鼠自主运动的训练装置 ,以记录小鼠每次主动运动时运动量的大小。方法 :用万能角铁建支架 ,镀锌筛网做跑笼 ,自行车的前轴用来做转轴 ,将转笼固定在支架上 ,共分 5层 ,每层固定 4个转笼 ,每个转笼都安装一个独立的红外线计数器 ,记录转圈数。结果 :小鼠在转笼中能自主运动 ,每小时平均跑 7～ 10m/min ;结论 :该装置能完整记录小鼠在无外界应激条件作用下自主运动的运动量的大小 ,可作为研究小鼠自主运动模型的训练装置。

木片压缩自动打包机的设计与研究

介绍了木片压缩自动打包机的结构和自动打包过程,并对其进行经济分析。

吸毒人员自愿戒毒相关因素Logistic分析

目的探讨吸毒人员自愿戒毒的相关因素,为有关部门控制艾滋病提供理论依据。方法通过现场流行病学方法进行调查。结果对968名吸毒人员进行调查,通过Logistic分析筛选出吸毒人员自愿戒毒的相关因素为年龄、民族、一个看上去健康的人会是艾滋病感染者、蚊虫叮咬是否会传染艾滋病、感染的孕妇会将艾滋病病毒传染给胎儿、性交时使用安全套可以预防艾滋病、由静脉注射吸毒转为口吸可以预防艾滋病、静脉注射毒品时间。结论吸毒人员自愿戒毒的相关因素主要是年龄、性别、静脉注射毒品时间和艾滋病预防传播知识。

石灰桩与土力学理论结合在工程纠偏中的应用

由于兰州某大学锅炉房地下管道的漏跑水,导致湿陷性黄土地基的不均匀沉降,使得其中的设备基础倾斜而影响其设备正常使用功能,为此需对地基进行相应处理.首先对事故现场进行检测,结合原有的部分工程施工图纸寻求事故产生的原因,其次基于相关土力学与地基基础的理论进行分析,并综合本地区类似事故的处理经验,提出了用石灰桩对地基土进行挤密并可能利用膨胀混凝土顶升的纠偏加固方案.在施工中实行全面过程监控,从而发现地基与提出的初步处理方案时所假设的条件有较大的差异,进一步根据现场的实际情况在原方案基础上利用土力学边坡滑移的理论实行定位开槽,从而实现了设备基础的自动纠偏的效果,较大程度地降低了工程施工费用.