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.

Had Better用法综述

“had better”的用法看似简单却常常被误用。为了避免“had better”的误用,本文综述了“had bet-ter”的用法,全面解释了“had better”的意思、用法、各种语法现象及相关问题。

Had Better短语的真实用法

就HadBetter短语的用法进行了分析、比较。通过阐述这一英语短语的语义与其汉语解释之间的矛盾 ,得出HadBetter短语用法相对复杂 。

The Struggle for Human Betterment as A theme in Mary(Shelly's) Frankenstein

Men struggle for human betterment for over thousands of years.In Mary Shelley’s Frankenstein,there are many heroes who struggle for human betterment,such as Frankenstein,the Modern Prometheus,Walton,and even the monster,etc.The better we live today the higher aims we want to reach in the future.In this Gothic novel,Mary Shelley shows her idea of the creation of life and the consequence of it.Some of her descriptions reflect her opinion.

半助动词Had better用法探微

从使用对象、体与态、句型转换、语用功能等方面对半助动词hadbetter进行了探讨 ,作者认为该半助动词已越来越口语化 ,使用时已无人称限制 ,在语用功能上 ,与特定词在特定语境 ,可以表达“劝导”以外的其它含义。

Can progesterone be a better alternative to dexamethasone for use in routine brain surgery?

Can progesterone be a better alternative to dexamethasone for use in routine brain surgery？ Surgical brain injury （SBI） is a form of brain trauma caused by various forms of neurosurgical interventions including brain tumor excision, evacuation of intracere- bral hemorrhage and brain lobectomv （e.g., in epililepsv surger）.

Abrasive Wear Characteristics of Carbon and Low Alloy Steels for Better Performance of Farm Implements

The low stress abrasion behaviours of heat treated mild, medium carbon and high C - low Cr steels, which are generally used in making farm implements, have been investigated. The simple heat treatment greatly improves the hardness, tensile strength and abrasion resistance of medium carbon and high C - low Cr steels. The results indicate that the material removal during abrasion is controlled by a number of factors, such as hardness, chemical composition, microstructure and heat treatment conditions. The conclusion is that the heat treated high C - low Cr steel and mild steel carburized by using coaltar pitch provide the best hardness and abrasion resistance and thus appear to be the most suitable materials for making agricultural tools.

西澳大利亚州立图书馆Better Beginnings早期识字计划实践与启示

Better Beginnings早期识字计划持续从世界各地的早期识字实践中学习,并将“每个孩子都准备好阅读”倡议纳入家庭培养孩子早期识字技能的方法中。西澳大利亚州立图书馆联合当地社区、学校和家庭深入参与到该计划中。项目针对不同年龄段的学龄前儿童提供了针对性的阅读内容与阅读方法。我国公共图书馆可借鉴其经验,构建精细化的学龄前儿童阅读推广分级体系与多主体合作机制,增强亲子共读娱乐性。

浅析had better的各种用法

had better是常见的习惯用语,意为“最好,必须”(should, must)。这个词组后面跟不带to的动词不定式。短语中的better并不涉及比较概念。除此之外,我们在使用中还应该注意以下几个问题: 一、had better后面通常接不带to的动词不定式的一般式,但有时也可以接不定式的完成式。这时往往含有“