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.

RMB:Depreciating Internationally while Appreciating Externally

RMB has been depreciating internally while appreciating externally since 2002. This new monetary phenomenon, has been strengthened in the context that US dollar depreciates internationally and that domestic economy is overwhelmed with excessive liquidities. Certainly the monetary phenomenon is the reflection of the actual economy: continued trade surplus, triggered robustly by the export-driven economy, is bringing a huge amount of exchange reserve which accelerates sequentially the expansion of domestic money supply. Furthermore, a refrained appreciation of RMB tends to deteriorate the domestic inflation, which is not simply a traditional concept of CPI but a broad inflation parameter including a variety of asset prices. It's sure that the new phenomenon is becoming a new challenge to the macroeconomic equilibrium as well as the decision maker of monetary policy.

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.

Ordinary Scenes Illustrate SpecialAtmosp here under Their Brushes——Appreciating the art ofZhou Tao,Chen Yi and Zhang Xin

In the past, many Chinese artists have beenafraid they would be criticized for lagging behindthe world in their conceptions. They are eager todo something unconventional or unorthodox. Theiractions are very similar with the trend Liu

劳动者创业与多层次社会保险需求——基于中国家庭金融调查数据的实证研究

采用2013—2017年中国家庭金融调查(CHFS)数据,实证检验自主创业对多层次社会保险需求的影响。研究发现:个体进行自主创业后的多层次社会保险需求会显著提高,增加幅度平均为3.29个百分点。相比于受雇个体,年龄在45~59岁且收入处于中等水平的发展型创业个体具有更高的多层次社会保险需求。在保险类型上,自主创业个体对多层次养老保险的需求最高,而对多层次医疗保险的需求较低。自主创业个体还会提高其家人的多层次社会保险需求,配置顺序依次是青壮年、老年人和儿童。

优化营商环境视域下城市土地再开发的法治困境与出路——以深圳市的实践探索为中心

为了缓解城市因土地、空间有限而按照传统的速度模式发展难以为继的窘境,盘活存量建设用地以推进营商环境优化至关重要。深圳市土地再利用制度探索实践极具特色,其制度构建的成败得失可供我国其他省市借鉴。通过考察深圳市土地再开发的实践探索可以发现,城市存量建设用地盘活利用时存在土地价值未得到公平分配、补偿时未合理区分合法建造与违法建筑、相邻地块权利人获得的土地利益差异显著和“合法外”土地的管理利用缺乏有效规制等制度困境,其主要根源在于历史遗留问题解决方案的上位法依据较为含糊、处理统征统转问题对法律规范的遵循尚有不足、不同制度中土地增值收益分配的基准存在差异和不同权利主体享有土地增值收益的比例失衡。在我国全面推进依法治国的新时代,完善盘活城市存量建设用地制度时,应当加快地方规范性文件的立、改、废,补强土地价值的公平分配规则;优化相关地方性制度,强调严格遵循保护合法利益的底线规则;同时,细化盘活城市存量建设用地的国土空间规划,明晰具体落实方案,并整理历史遗留问题的类型,对现行地方性制度进行靶向修正。

“鉴定—欣赏”:教育评价范式的双重结构及其实现——基于拔尖创新人才培养的思考

与“全面而有个性的发展”的培养目标相适应,教育评价应当是由“鉴定性评价”和“欣赏性评价”组成的双重结构。鉴定性评价是指对学生素质按照预先设定的指标和标准进行评定,重在发现学生素质的不足,以“补短”推动“全面发展”;欣赏性评价是指根据学生素质发展的实际情况建构指标和标准并据此进行评定,重在发现学生素质的优势,以“扬长”促进“个性发展”。二者各有优劣,既不可彼此取代,亦需要相互补充。当前,“鉴定”几乎成为教育评价的唯一范式,评价改革基本集中于“鉴定”内部的评价内容调整,学生的个性潜质和特殊才能很难有机会被发现,更谈不上培养。为了真正实现学生的全面而有个性的发展,培养出拔尖创新人才,教育评价亟须从鉴定性评价的单一范式结构转向“鉴定—欣赏”性评价的双重范式结构。具体策略包括:确立注重特长的人才观,构建多元录取的考试招生模式,善用彰显个性的评价方法和提供专业且过硬的评价保障等。

非国有董事治理积极性与国有企业审计意见决策

如何发挥非国有董事治理积极性以提高国有企业审计意见质量,实现国有资产保值增值,是深化国有企业改革亟需研究的问题。以2013—2022年A股国有企业上市公司为样本,构建离散选择模型,以投非赞成票代理非国有董事治理积极性。研究发现,非国有董事治理积极性越高,越能够提示应计盈余管理风险,审计师越倾向出具非标准审计意见;第一大非国有股东持股比例越高,混改国有企业股权集中度越低,非国有董事治理积极性与审计意见谨慎性的关系越显著。经过稳健性检验,研究结论不变。进一步研究发现,非国有董事积极履行治理职能导致审计意见质量提高有利于混改国有企业资产保值增值,实现国有企业混改主要目标。研究结论对于国有企业审计师正确决策审计意见、非国有董事有效维权、政府监管部门和混改国有企业充分尊重非国有董事治理的积极性、实现国有企业改革目标,均具有启示意义。

黑龙江彩陶艺术制品经济价值及产业化发展探析

以彩陶艺术制品的文化性、地域性为前提,以泥料和技艺为基础,实现黑龙江彩陶艺术制品文化与审美及其经济价值研究目的。采用审美性与经济性结合研究方法,考察彩陶艺术制品的审美性、实用性和多元文化性。从彩陶艺术制品经济价值出发,重点剖析彩陶艺术制品的商品性质,解析彩陶艺术制品价值决定其经济效益的源泉,从经济视域为彩陶艺术制品发展定位,明确提出其产业化生产的研究结果;阐述黑龙江彩陶艺术制品产业化路径和产业化发展目标,探究彩陶艺术制品的品牌形象塑造、品牌建设和多渠道销售。

艺术媒介研究的三重理论变奏

对艺术媒介的研究不能只停留在单一的维度,而应该从三种维度同时进行。本质论维度的研究认为,艺术媒介是对特定的物质材料进行一定的赋形后所形成的符号体系,这构成了我们对艺术媒介的基本认识;创作论维度的研究揭示了艺术媒介在贯通内在构思与外在表现整个过程中不可或缺的积极作用,指出媒介不仅会影响艺术家的审美感知,而且会影响其艺术创作的方式;鉴赏论维度的研究肯定了艺术媒介自身的价值,认为媒介和形式自身的品质就足以引起观者的审美愉悦。围绕艺术媒介所展开的三重维度的研究不仅有助于揭示艺术媒介的本质、特征和功能,而且对艺术媒介理论的建构颇具奠基意义。