Research on the Reform of the Course“Reading of Concrete Structure Plan and Construction Drawings”Under the Background of“Promoting Teaching and Learning Through Competitions”

The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.

Learning Vector Coding Methods of ART1 and Their Applications

As one of the unsupervised learning models, ART1 has been widely used in data mining or other fields, while coding of it’s learning vector is very important. Their input vector coding methods and learning vector coding methods are described in detail. The corresponding applications are given.

A SPEECH RECOGNITION METHOD USING COMPETITIVE AND SELECTIVE LEARNING NEURAL NETWORKS

On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.

Benefit to Chinese High School Students' Academic Learning Outcome and Evaluation by Comparing Two Different Assessment Systems

More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present paper, I will try to emphasis that collaborative assessment is positive to high school students' learning outcome, and cooperative and competitive assessment should be mixture implemented in Chinese education process rather than only using competitive assessment to evaluate students' achievement. I hope that Chinese students and teachers could teach or test in the less pressure by reforming the educational evaluation system.

The Role of the Finnish Basic Education Model in Enhancing Students’Happiness and Learning Motivation

This paper examines the Finnish basic education model,renowned for its student-centric approach and high performance in international assessments.The study explores the model’s core principles,including equity,teacher autonomy,and minimal standardized testing,and their impact on student happiness and motivation.Through case studies,interviews,and surveys with students,teachers,and parents,the paper provides an in-depth analysis of the Finnish model’s effectiveness.Challenges such as adaptability to diverse cultural contexts,integration of immigrant students,and sustainability in the face of global educational trends are also discussed.The paper concludes with recommendations for the continued evolution of the Finnish model,emphasizing the need for adaptability,inclusivity,and a focus on sustainability and technology integration.

A STUDY OF METHODS FOR IMPROVING LEARNING VECTOR QUANTIZATION

Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional

R2N: A Novel Deep Learning Architecture for Rain Removal from Single Image

Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.

企业E-Learning导入浅谈

介绍了企业E-Learning的概念,探讨了企业导入E-Learning的原因,阐述了什么样的企业需要导入E-Learning。

Identification of Fuzzy System Via Fuzzy Competitive Learning Method

The paper presents an approach to identfying a fhzzy model composed of fuzzy-logic rules for a multi-in-put/single outpu system. The ther of fuzzy rules and membership functions of input variables are obtained by means of a fuzzy competitive lerning method with a validity criterion. This method avoids the complexity of system structure identilication and decreases the number of fuzzy rules. Recareive least square algorithm can be used to iden-tify the parameters of conclusion polynomials .The proposed method is used to identify the well-known Box-Jenkins da-ta set with the result shawn at the end of the paper to demonstrae its advanages.

Research of Dynamic Competitive Learning in Neural Networks

Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning process. Comparing model with other typicalclassification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevelclassification of the input pattern with an optional accuracy and gives a strong support possibilityfor the parallel computational main processor. The idea is suitable for the high-level storage ofcomplex datas structures for object recognition.

Image segmentation based on competitive learning

Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.

Improvement of Stochastic Competitive Learning for Social Network

As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.

Creating Optimal Language Learning Environment

From the viewpoint of psycholinguistics, this paper concerns how to create an optimal language learning environment in language learning, to stimulate students enthusiasm to participate in classroom activities and to make language learning easier and more pleasant.

Organizational Learning （OL） as a Competitive Advantage

This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals. The most significant finding is intended to highlight the guidelines for each of researchers＇ concentrated cluster and to demonstrate that different researchers present different guidelines for processes, individual skills, and changes in the environment, teamwork, and competitiveness. The insight, gained by considering OL as a process, is not routine It allows one to create, acquire, and transfer knowledge. This will always be limited to the internal capabilities developed during the course of the timeline and will identify skills and competencies generated in accordance with the requirements presented by different environments. OL is associated with both the change in organizational behaviors and the creation of a knowledge base.

Discriminative training of GMM-HMM acoustic model by RPCL learning

This paper presents a new discriminative approach for training Gaussian mixture models(GMMs)of hidden Markov models(HMMs)based acoustic model in a large vocabulary continuous speech recognition(LVCSR)system.This approach is featured by embedding a rival penalized competitive learning(RPCL)mechanism on the level of hidden Markov states.For every input,the correct identity state,called winner and obtained by the Viterbi force alignment,is enhanced to describe this input while its most competitive rival is penalized by de-learning,which makes GMMs-based states become more discriminative.Without the extensive computing burden required by typical discriminative learning methods for one-pass recognition of the training set,the new approach saves computing costs considerably.Experiments show that the proposed method has a good convergence with better performances than the classical maximum likelihood estimation(MLE)based method.Comparing with two conventional discriminative methods,the proposed method demonstrates improved generalization ability,especially when the test set is not well matched with the training set.

Hawk and pigeon's intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

For unmanned aerial vehicle(UAV)swarm dynamic combat,swarm antagonistic motion control and attack target allocation are extremely challenging sub-tasks.In this paper,the competitive learning pigeon-inspired optimization(CLPIO)algorithm is proposed to handle the cooperative dynamic combat problem,which integrates the distributed swarm antagonistic motion and centralized attack target allocation.Moreover,the threshold trigger strategy is presented to switch two sub-tasks.To seek a feasible and optimal combat scheme,a dynamic game approach combined with hawk grouping mechanism and situation assessment between sub-groups is designed to guide the solution of the optimal attack scheme,and the model of swarm antagonistic motion imitating pigeon’s intelligence is proposed to form a confrontation situation.The analysis of the CLPIO algorithm shows its convergence in theory and the comparison with the other four metaheuristic algorithms shows its superiority in solving the mixed Nash equilibrium problem.Finally,numerical simulation verifis that the proposed methods can provide an effective combat scheme in the set scenario.

Application of a soft competition learning method in document clustering

Hard competition learning has the feature that each point modifies only one cluster centroid that wins. Correspondingly, soft competition learning has the feature that each point modifies not only the cluster centroid that wins, but also many other cluster centroids near this point. A soft competition learning method is proposed. Centroid all rank distance （CARD）, CARDx, and centroid all rank distance batch K-means （CARDBK） are three clustering algorithms that adopt the proposed soft competition learning method. Among them the extent to which one point affects a cluster centroid depends on the distances from this point to the other nearer cluster centroids, rather than just the rank number of the distance from this point to this cluster centroid among the distances from this point to all cluster centroids. In addition, the validation experiments are carried out in order to compare the three soft competition learning algorithms CARD, CARDx, and CARDBK with several hard competition learning algorithms as well as neural gas （NG） algorithm on five data sets from different sources. Judging from the values of five performance indexes in the clustering results, this kind of soft competition learning method has better clustering effect and efficiency, and has linear scalability.

Kernel and graph： Two approaches for nonlinear competitive learning clustering

Competitive learning has attracted a signif- icant amount of attention in the past decades in the field of data clustering. In this paper, we will present two works done by our group which address the nonlin- early separable problem suffered by the classical com- petitive learning clustering algorithms. They are ker- nel competitive learning （KCL） and graph-based multi- prototype competitive learning （GMPCL）, respectively. In KCL, data points are first mapped from the input data space into a high-dimensional kernel space where the nonlinearly separable pattern becomes linear one. Then the classical competitive learning is performed in this kernel space to generate a cluster structure. To real- ize on-line learning in the kernel space without knowing the explicit kernel mapping, we propose a prototype de- scriptor, each row of which represents a prototype by the inner products between the prototype and data points as well as the squared length of the prototype. In GM- PCL, a graph-based method is employed to produce an initial, coarse clustering. After that, a multi-prototype competitive learning is introduced to refine the coarse clustering and discover clusters of an arbitrary shape. In the multi-prototype competitive learning, to gener- ate cluster boundaries of arbitrary shapes, each cluster is represented by multiple prototypes, whose subregions of the Voronoi diagram together approximately charac- terize one cluster of an arbitrary shape. Moreover, we introduce some extensions of these two approaches with experiments demonstrating their effectiveness.

基于学科竞赛与产教融合的创新创业人才培养探讨

文章探讨了基于学科竞赛与产教融合的创新创业人才培养理念,介绍了竞赛活动在激发学生创新思维、提高学生实践能力方面的重要作用,分析了产教融合的必要性和优势,指出校企合作是推进产教融合的主要途径,并提出了培养创新创业人才的举措。以赛促学和产教融合是应对时代变革、满足经济发展需要的重要途径,有利于推动高等教育转型升级,也有助于培养具有创新能力和创业精神的人才。

新能源汽车电池回收网点竞争选址模型及算法

针对考虑排队论的新能源汽车电池回收网点竞争设施选址问题,提出一种改进的人类学习优化(IHLO)算法。首先,构建包含排队时间约束、容量约束和门槛约束等条件的新能源汽车电池回收网点竞争设施选址模型;然后,考虑到该问题属于NP-hard问题,针对人类学习优化(HLO)算法前期收敛速度较慢、寻优精度不够高、求解稳定性不够高的不足,通过引入精英种群反向学习策略、团队互助学习算子和调和参数自适应策略提出IHLO算法;最后,以上海市和长江三角洲为例进行数值实验,并将IHLO算法和改进二进制灰狼(IBGWO)算法、改进二进制粒子群(IBPSO)算法、HLO算法和融合学习心理学的人类学习优化(LPHLO)算法进行比较。大、中、小三种不同规模的实验结果表明,IHLO算法在15个指标中的14个指标上表现最优,IHLO算法比IBGWO算法求解精度至少提高了0.13%,求解稳定性至少提高了10.05%,求解速度至少提高了17.48%。所提算法具有较高的计算精度和优化速度,可有效解决竞争设施选址问题。