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.

博士生能力发展:学术志向与攻博满意孰轻孰重?

借助2019年Nature全球博士生调查数据,本研究探讨了博士生谋求能力发展,面临“学术志向与攻博满意孰轻孰重?”两难问题时的行动抉择。结果显示,就能力发展而言,博士生攻博满意与学术志向是促进性而非替代性关系。从中、美比较视角看,中国博士生的能力发展更依赖其持有的学术志向,美国博士生的攻博满意对促进其能力发展的正向效应则更高。对此,研究围绕博士生、学校、社会等博士生教育的利益相关者进行讨论,将该议题延展为公共议题并提出建议。

基于扎根理论的学生能力图谱构建与提升进路——以旅游管理专业为例

新文科建设的核心要义是立足新时代,回应新需求,对传统文科革故鼎新,打破学科专业壁垒,培育高素质文科人才。在此背景下,对30位南宁师范大学旅游管理专业的在校生、毕业生和专业课教师进行深度访谈,采用扎根理论的研究方法对转录后的文本资料进行三级编码,构建知识应用能力、跨界创新能力、协调沟通能力、人文伦理素养4个维度的学生能力图谱,并对其能力特征进行了具体阐释。在此基础上,提出培养跨学科视野,提升知识应用能力;树立跨思维理念,增强跨界创新能力;促进合作跨越发展,提升沟通协调能力;探索文化交流,提升人文伦理素养的建议。

基于OBE理念的焊接专业核心课程教学探索

OBE模式是一种以学生产出为导向的教学模式,将其理念引入焊接专业核心课程的教学中,解决传统教学存在的模式陈旧、内容枯燥等问题,从教学设计分析、教学模式改进和考核评价等方面进行课程教学和实践探索,最终达到调动学生积极性并提高学生能力的目的。

创业教育与专业教育的有机融合:大学生能力培养的有效路径

如何更加有效地培养大学生的创新能力和创业能力,已成为我国高等教育的重要使命。以专业教育为基础,结合创业教育的实践性、前瞻性、跨学科性特征,推进两者的有机融合是培养大学生能力的有效路径。为此,我国高校应科学界定中国特色的创业教育,构建中国式的创业教育三螺旋体系,加强"双师型"教师队伍建设,选取差异化的融合模式与路径,营造鼓励创业的大学文化,最终实现创业教育与专业教育的有机融合。

基于提高学生实践技能的近代物理实验教学质量评价及监控体系建设与实践

分别从教学计划、教学内容和教学过程监控,教学方式和学生实践监控,成绩评价等几个方面介绍了山西师范大学物理与信息工程学院在基于提高学生实践技能的近代物理实验教学质量评价及监控体系建设与实践方面的探索。在有效提高实验教学质量、加强实践教学环节,使学生的创新精神、实践能力和创新创业能力大幅提升方面做出了积极贡献。

应用型本科高校学生能力达成评价体系构建问题研究

针对目前应用型本科高校学生能力达成评价体系构建过程中存在的不足,提出从培养目的到能力目标再到效果评价的构建逻辑过程,并在分析评价体系的构建原则、关键问题的基础上,具体提出应用型本科高校学生能力达成评价体系的构建思路。

计算机操作系统的教学与学生能力的培养

文章总结教学实践,从理论上进行提高,探讨了计算机操作系统的教学与学生能力的培养方法。