Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.

基于Self-CGRU模型的地铁基坑周边地表沉降预测

为提升地铁基坑开挖引发的地表沉降的预测精度,基于自注意力机制和深度学习提出一种能捕捉沉降数据时空特性的深度注意力组合预测模型(self-attention convolutional gated recurrent units,Self-CGRU)。Self-CGRU模型由空间模块和时间模块搭建。空间模块中,选择卷积神经网络捕捉相邻监测点沉降数据的空间相关性;时间模块中,使用门控循环单元神经网络分析沉降数据的时间规律,并引入自注意力机制捕获沉降数据内部的自相关性,进而得到沉降预测值。选取中国深圳市地铁基坑周边地表沉降数据验证Self-CGRU模型,结果表明:相比现有模型,Self-CGRU模型预测性能更好,使预测精度提高了17.48%~29.17%。研究成果可为地铁基坑周边地表沉降预测提供一种准确且稳定的新模型。

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

Research on Application of Metacognitive Strategy in English Listening in the Web-based Self-access Learning Environment

Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.

An Experimental Study on the Development of Self-access Language Learning by Non-English Majors

Self-access language learning has attracted much attention in second language teaching and researching. This paper aims to do some researches on developing self-access language learning in the self-access center and test its effect on developing learner autonomy. Data,collected in the form of questionnaires and interview,were analyzed. Results show the development of self-access language learning by non-English majors.

Effectiveness of the flipped classroom on the development of self-directed learning in nursing education:a meta-analysis

Objectives: To examine the best practice evidence of the effectiveness of the flipped classroom（FC） as a burgeoning teaching model on the development of self-directed learning in nursing education.Data sources: The relevant randomized controlled trial（RCT） and non-RCT comparative studies were searched from multiple electronic databases including PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature（CINAHL）, Cochrane Central Register of Controlled Trials（CENTRAL）, Wanfang Data, China National Knowledge Infrastructure（CNKI）, and Chinese Science and Technology Periodical Database（VIP） from inception to June 2017.Review methods: The data were independently assessed and extracted for eligibility by two reviewers. The quality of included studies was assessed by another two reviewers using a standardized form and evaluated by using the Cochrane Collaboration’s risk of bias tool. The self-directed learning scores（continuous outcomes） were analyzed by using the 95% confidence intervals（Cls） with the standard deviation average（SMD） or weighted mean difference（WMD）. The heterogeneity was assessed using Cochran’s I;statistic.Results: A total of 12 studies, which encompassed 1440 nursing students（intervention group = 685, control group = 755）, were eligible for inclusion in this review. Of 12 included studies, the quality level of one included study was A and of the others was B. The pooled effect size showed that compared with traditional teaching models, the FC could improve nursing students’ selfdirected learning skill, as measured by the Self-Directed Learning Readiness Scale（SDLRS）, Self-Directed Learning Readiness Scale for Nursing Education（SDLRSNE）, Self-Regulated Learning Scale（SRL）, Autonomous Learning Competencies scale（ALC）, and Competencies of Autonomous Learning of Nursing Students（CALNS）. Overall scores and subgroup analyses with the SRL were all in favor of the FC.Conclusions: The result of this meta-analysis indicated that FCs could improve the effect of self-directed learning in nursing education.Future studies with more RCTs using the same measurement tools are needed to draw more authoritative conclusions.

Relationship between self-directed learning readiness, learning attitude, and self-efficacy of nursing undergraduates

Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.

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.

Study on Self-consciousness of Children With Learning Disabilities and Related Factors

Objective To study the self-consciousness of children with learning disabilities (LD) and to identify related factors. Methods Five hundred and sixty pupils graded from 1 to 6 in an elementary school were investigated. According to the pupil rating scale revised screening for learning disabilities (PRS), combined Raven’s test (CRT) and achievement of main courses, 35 of 560 pupils were diagnosed as LD children. Thirty-five children were selected from the average children and 35 from advanced children in academic achievement equally matched in class, gender, and age with LD children as control groups. The three groups were tested by Piers-Harris children’s self-concept scale. Basic information of each subject was collected by self-made questionnaire. Results Compared with the average and advanced children, LD children got significantly lower scores in self-concept scale. Based on logistic regression analysis, 3 factors were identified, including family income per month, single child and delivery model. Conclusion The results suggest that self-consciousness of children with LD is lower than that of normal children.

A Study of English Learning Motivation of Left-behind Children from the Perspective of L2 Motivational Self System

A survey carried out among 286 left-behind children in a middle school through the lens of L2 Motivational Self System presents that although left-behind children possess low level of English learning motivation,they are favorably disposed towards studying English.Among the three dimensions of L2 Motivational Self System in this survey,L2 Learning Experience influences English learning motivation of left-behind children most and Ideal L2 Self with Ought-to L2 Self ranks the second in affecting their motivation.Practicalimplications are also provided.

Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

The effect of self-observation on the self-directed learning ability of nursing students: An experimental study

Background: Patients expect nurses to be both technically competent and psychosocially skilled. Enhancing the quality of patient care and patient safety in healthcare settings has increased, resulting in limited opportunities for students to practice clinical skills in healthcare settings. Achieving competence in these skills is viewed as an essential task to be completed during the school curriculum. Objective: The purpose of this study was to evaluate the use of self-observation through cellular recordings as an adjunct to the clinical skills teaching of a blood sugar test to undergraduate nursing. Design: The research design consisted of pre- and post-test consecutive experimental design through a control group. Settings: This study targeted nursing students enrolled in baccalaureate programs running in Korea. Participant: The participants were 64 students including 34 for the experimental group and 30 for the control group. Methods: Those in the control group received standard teaching methods using lectures and skills classes and facilitated the use of self-study methods. Those in the experimental group received standard teaching using lectures and skills classes and facilitated use of cell phone recorded self-observation. The self-confidence of practicing a blood sugar test, satisfaction with the learning method, self-study participation, level of interest in nursing practice, and self-directed learning ability were measured using questionnaires. Results: Significant between-groups differences were detected in self-confidence of practicing a blood sugar test (t = 2.067, p = 0.043), satisfaction with the learning method (t = 2.818, p = 0.044), self-study participation (χ2 = 7.635, p = 0.022), and average self-directed learning ability (t = 3.202, p = 0.002). Conclusions: Self-observation through cellular phone recordings is an effective learning method as an adjunct to teach clinical skills.

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.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

Nursing Students’ Readiness for Self-Directed Learning and Its Effect on Learning Outcome in South-West Nigeria

Self-directed learning (SDL) uses diverse learning resources to solve identified problems in learning. Nursing is a lifelong learning profession and SDL is a valuable skill to remain relevant and productive professionals. Nursing students are expected to embrace SDL and develop these skills. However, there has been no evidence of this innovative process in South-West Nigeria. This study seeks to evaluate nursing students’ readiness for SDL and its effect on learning outcome. This quasi-experimental study purposively utilized 229 nursing students as participants. Baseline (P1) data was collected using Gugliemino’s SDL readiness scale (SDLRS) and a validated-structured questionnaire. Participants had a pre-test to assess knowledge at P1 followed by 6 weeks interaction using SDL on selected topics in Medical-surgical nursing and the same test at post-intervention (P2). Using a 50-point scale, knowledge was categorized as good ≥ 25 and poor < 25 and SDLRS on a 290-point scale was categorized as below average 5 - 201, average 202 - 226 and above average 227 - 290. Descriptive statistics, Chi-square test, t-test and linear regression analysis were used for analysis at p = 0.05. Nursing students’ SDLRS was average;mean = 203 ± 23.0. A significant difference exists between nursing students with good knowledge at P1 and P2. At P1, 39.2% had good knowledge, mean = 22.2 ± 6.3, and 90.1% at P2, mean = 30.6 ± 5.4, p < 0.05 also a significant relationship exist between SDLR and learning outcome at P2;p < 0.05. With the nursing students’ average SDL readiness level having a significant effect on learning outcome. Nursing training institutions should provide necessary resources to embrace SDL as a main-line teaching method to ensure competent life-long professionals.

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.

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.