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.

Empirical Studies of the Factors Influencing Farmers' Enthusiasm for Growing Grain

To ensure the future food self-sufficiency in China,it is necessary to mobilize producers' enthusiasm for growing grain. Theoretically,it is mainly influenced by economic interests,land scale,farmers' characteristics and agricultural support policy. Through field research of farmers,we use model to explore these influencing factors. The results show that the dwindling arable land making grain cultivation fail to form economies of scale is a key factor restricting farmers' enthusiasm for growing grain; farmers' age and the share of agricultural labor in the family are important factors restricting their grain growing; agricultural support policy can stimulate the enthusiasm for growing grain to a certain extent. Therefore,there is a need to promote the concentration of land and improve various agricultural support policies,in order to improve the economic benefits of growing grain and encourage qualified young workers to engage in grain production activities.

Analysis of Factors Influencing Forest Farmers' Enthusiasm for Forest Culture and Management after the Completion of Reform of Collective Forest Right System

Taking Jiangshan City in Zhejiang Province for example,this article uses the binary logit choice model based on the field survey data,to study the factors influencing forest farmers'enthusiasm for forest culture and management after the completion of reform of collective forest right.Finally the following recommendations are put forth:further improving and implementing the forest ecological benefit compensation fund system;reforming the felling management mode and gradually establishing the sustainable forest management system based on forest management plan;improving the technology,market and information services to strengthen the forestry science and technology support;developing the specialty industries such as the bamboo industry and oil-tea camellia industry;developing the under-forest economy and cultivating the underforest industries with characteristics based on the local circumstances.

Institutional Arrangement to Improve Farmers' Enthusiasm

On the basis of summarizing theories related to public goods,with the aid of trend curve of China's annual grain yield from 1978 to 2010,in combination with grain policies at that time,empirical analysis is carried out on institution as key factor influencing the farmers' enthusiasm,showing that there is highly positive correlation between institutional change and grain production.In order to raise farmers' production enthusiasm and ensure the national grain security,this paper puts forward appropriate countermeasure systems from the point of view of innovating grain production policies.The countermeasure systems include:firstly,innovate agricultural operation institution;secondly,improve agricultural personnel cultivation mechanism;thirdly,complete financial investment system;fourthly,set up grain security protection system;and finally,build risk support system.

Fueled With Enthusiasm: Running for the Future

On November 2, 2017, during the Mercedes-Benz International Fashion Week in China, the finals for ＂Qiaodan Cup -- 12th China Sportswear Design Contest＂ was held in the first workshop of 751 D PARK in Beijing.

Views on the Negative Factors that Affect Students’Enthusiasm in Class

This thesis mainly discusses about the negative factors that affect the student'senthusiasm in class. It is separated into two parts. In the first part, it explores the reason from the aspect of economy through examples and comparison. In the other part, it narrates the defects of traditional teaching methods which have some significant connections with students 'enthusiasm.

高标准农田建设提升了农户种粮积极性吗?——基于双季稻种植的考察

高标准农田建设是增强粮食综合生产能力,强化国家粮食安全保障的重要举措。基于江西省1556份微观农户调查数据,采用双季稻种植行为来衡量农户种粮积极性,实证分析高标准农田建设对传统双季稻主产区农户种粮积极性的影响及其作用机制。研究发现:(1)高标准农田建设显著提高了农户种粮积极性,促进传统双季稻主产区农户种植双季稻,并扩大双季稻种植规模,该结论在进行稳健性检验后依然成立。(2)机制分析表明,促进农业社会化服务发展、降低农业生产成本、诱导农地转入是高标准农田建设提高农户种粮积极性的三个重要路径。(3)异质性分析发现,高标准农田建设对农地调整、低细碎化与非平原地区农户的双季稻种植行为有更明显的促进作用。为进一步强化粮食安全保障,更好地发挥高标准农田建设的种粮激励效应,应持续大力推进高标准农田建设,优化高标准农田建设模式,着力降低高标准农田的细碎化程度。

快速康复理念下家庭参与式护理模式在 肺结节围术期患者护理中的应用

目的观察肺结节围术期护理中实施快速康复理念下家庭参与式护理模式的效果。方法选取行手术治疗的肺结节患者180例,采取随机数字表法分为对照组和观察组,每组90例。对照组患者应用常规护理,观察组实施快速康复理念下家庭参与式护理模式。比较2组患者术后时间结局指标、术前焦虑情况、家庭负担及术后训练积极性。结果观察组患者胸管留置时间及住院总时间均短于对照组,差异有统计学意义(P<0.05);干预后,观察组患者阿姆斯特丹术前焦虑与信息量表(APAIS)及疾病家庭负担量表(FBS)得分低于对照组,差异有统计学意义(P<0.05);观察组患者术后训练积极度水平高于对照组,差异有统计学意义(P<0.05)。结论实施快速康复理念下家庭参与式护理模式有利于促进肺结节手术患者早期康复,缓解患者术前焦虑情况,减轻家庭负担,提高患者术后康复训练积极性。

Meeting New Challenges with Full Enthusiasm

The Chinese Academy of Sci-ences (CAS) recently held its 2001annual work conference. At theconference, experience gainedthrough the first-phase trial imple-mentation of the Knowledge Inno-vation Program (KIP) was summedup and shared, and ways were de-vised to advance the second-phasetrial implementation.

基于产权视角的政府投资农村水利设施可持续供给研究

随着乡村振兴的不断推进,农村水利设施的重要性愈发凸显。为探究如何提高设施的可持续供给水平、实现政府投资农村水利设施持续性高质量供给,基于案例分析,梳理总结出产权明晰度、运行秩序合理性和农民管护积极性是政府投资农村水利设施供给可持续性的主要影响因素,并结合产权理论、交易费用理论、集体行动理论、制度分析与发展框架等理论,构建了产权明晰度、运行秩序合理性、农民管护积极性对政府投资农村水利设施供给可持续性影响机理的理论模型。基于对华东地区多个省份农民的问卷调查,运用SPSS 26.0和Amos 24.0软件进行分析,结果表明:(1)产权明晰度、运行秩序合理性、农民管护积极性分别与政府投资农村水利设施供给可持续性正相关;(2)运行秩序合理性、农民管护积极性在产权明晰度与政府投资农村水利设施供给可持续性之间存在中介作用,农民管护积极性在运行秩序合理性与供给可持续性之间存在中介作用。