网戏人与网学人的知识沟在急剧扩大

提出了网戏人与网学人的概念,网戏人只把网络当玩具,而网学人把网络当工具。文化教育程度及求知动机的不同是网戏人和网学人知识沟差距加大的最关键原因。通过分析比较中外网民的相关数据,认为大多数中国网民身为网戏人而不知,与国外网民大多把网络当工具的网学人状态存在着巨大的差距。

Prediction of mechanical properties of A357 alloy using artificial neural network

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.

Improvement and application of neural network models in development of wrought magnesium alloys

Neural network models of mechanical properties prediction for wrought magnesium alloys were improved by using more reasonable parameters, and were used to develop new types of magnesium alloys. The parameters were confirmed by comparing prediction errors and correlation coefficients of models, which have been built with all the parameters used commonly with training of all permutations and combinations. The application was focused on Mg-Zn-Mn and Mg-Zn-Y-Zr alloys. The prediction of mechanical properties of Mg-Zn-Mn alloys and the effects of mole ratios of Y to Zn on the strengths in Mg-Zn-Y-Zr alloys were investigated by using the improved models. The predicted results are good agreement with the experimental values. A high strength extruded Mg-Zn-Zr-Y alloy was also developed by the models. The applications of the models indicate that the improved models can be used to develop new types of wrought magnesium alloys.

INVERSE KINEMATICS FOR A 6 DOF MANIPULATOR BASED ON NEURAL NETWORK

A methodology is presented whereby a neural network is used to learn the inverse kinematic relationships of the position and orientation of a six joint manipulator. The arm solution for the orientation of a manipulator using a self organizing neural net is studied in this paper. A new training model of the self organizing neural network is proposed by thoroughly studying Martinetz, Ritter and Schulten′s self organizing neural network based on Kohonen′s self organizing mapping algorithm using a Widrow Hoff type error correction rule and closely combining the characters of the inverse kinematic relationship for a robot arm. The computer simulation results for a PUMA 560 robot show that the proposed method has a significant improvement over other methods documented in the references in self organizing capability and precision by training process.

Wavelet network solution for the inverse kinematics problem in robotic manipulator

Wavelet network, a class of neural network consisting of wavelets, is proposed to solve the inverse kinematics problem in robotic manipulator. A wavelet network suitable for dealing with multi-input and multi-output system is constructed. The network is optimized by reducing the number of wavelets handling large dimension problem according to the sample data. The algorithms for sparseness analysis of input data and fitting wavelets to the output data with orthogonal method are introduced. Then Levenberg-Marquardt algorithm is used to train the network. Simulation results showed that this method is capable of solving the inverse kinematics problem for PUMA560.

Attitude of nursing students towards Internet-based nursing services for the aged

Objective To investigate the attitude,willingness,and motivation of third-year undergraduate nursing students from a university of Chinese medicine toward Internet-based nursing services for the aged(IBNSA),providing a reference for its development and related nursing education.Methods This study was conducted from March to April 2019.Using a self-designed questionnaire,this cross-sectional study comprised 508 third-year undergraduate nursing students from a university of Chinese medicine.The questionnaire was divided into two parts.The first part contained the general information of nursing students.The second part investigated nursing students’attitudes,willingness,and motivation towards IBNSA(a total of seven questions).Results Of the 508 nursing students,314(61.81%)expressed support for the IBNSA,44(8.66%)expressed disapproval.Regarding career choice,279(54.92%)were willing to choose IBNSA,51(10.04%)were unwilling.The top three motivations for choosing IBNSA as a nursing student career were increased income,high autonomy and flexibility,and good job prospects.On the contrary,the top three reasons not to choose this option were safety concerns,lack of time and energy,and unwillingness to undertake elderly care.Experience with community activities or a part-time job,experience with elderly care,willing to choose the nursing profession,willing to engage in nursing care after graduation,and willing to engage in nursing for elderly patients after graduation were significantly associated with the motivation of nursing students to participate in IBNSA(P<0.05).Conclusion The majority of nursing students have a positive attitude towards IBNSA and are willing to choose it as their future career.Nurses’and patients’safety guarantees and salary distribution were critical factors influencing their choice.Nursing schools should pay attention to the concerns of nursing students,constantly improve the management system of IBNSA,strengthen safety education,and provide professional knowledge and skills to improve the quality of personnel training.

A Proposal of Adaptive PID Controller Based on Reinforcement Learning

Aimed at the lack of self-tuning PID parameters in conventional PID controllers, the structure and learning algorithm of an adaptive PID controller based on reinforcement learning were proposed. Actor-Critic learning was used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to improve the learning efficiency, a single RBF neural network was used to approximate the policy function of Actor and the value function of Critic simultaneously. The inputs of RBF network are the system error, as well as the first and the second-order differences of error. The Actor can realize the mapping from the system state to PID parameters, while the Critic evaluates the outputs of the Actor and produces TD error. Based on TD error performance index and gradient descent method, the updating rules of RBF kernel function and network weights were given. Simulation results show that the proposed controller is efficient for complex nonlinear systems and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

Semiotics of the Political Image： The 2012 Mexican Election

This study will attempt to carry out a semiotic analysis of the images that proliferated during the 2012 electoral process in Mexico, from the pre-carnpaign period to June 19, 2012. The analysis carried out here is based on the categories proposed by Eliseo Ver6n pertaining to the study of political images--from analysis of the semiological image to media discoursivities--which fit within the framework of the digital public sphere and participative visual activism. The official candidate websites are taken as sample, along with two Facebook sites which summarize critical images as well as the independent informational sites created by the #YoSoy132 movement.

Simulating the Inverse Kinematic Model of a Robot through Artificial Neural Networks： Complementing the Teaching of Robotics

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs （artificial neural networks） in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

Reflections on the Dilemma and Solutions of College Students’ Innovation and Entrepreneurship Education under the Background of “Internet Plus”

Appropriate innovation and Entrepreneurship Education in the university can effectivelypromote the reform of teaching mode and improve the quality of personnel training. With the emergence of the “InternetPlus”, it is inevitable to integrate the Internet and innovation educationorganically. In this paper, on the basis of the existing literature and data review as well as the field researches in some universities, the author sorted out the plight of the present situation of College Students’ innovation and Entrepreneurship Education under the background of the “InternetPlus”, expounded the function and significance of innovation and entrepreneurship education, and puttedforward some suggestions to the construction of universities’ innovation and entrepreneurship education system, to promote students’ overall growh and social progress.

The analysis of adult English vocabulary learning strategies based on network

The article which is on the basis of English learning and adult learning characteristics, propose adult English vocabulary learning strategies based on network, and it thinks that adult learners are based on making full use of network to provide boundary, it overcomes the flaws of adult English and resist the temptation of other information network, and it takes the right vocabulary memory strategies, cognitive strategies, vocabulary learning metacognitive strategies, affective strategies of vocabulary learning and vocabulary learning society strategy ,then it can achieve good vocabulary learning effect.

Challenge and Revelation Massive Open Online Course Brings to the British and American Literature Teaching

Massive Open Online Course （MOOC） has many characteristics, such as mass, openness, and online learning, which can effectively make up for the problem ＆shortage of teachers in the college English teaching. But, there is also a challenge to introduce MOOC to the college English teaching. This paper analyzes the challenge and revelation MOOC brings to college English teaching combined with the characteristics of MOOC, and points out that the role of teachers, students, and managers should be changed under the background of MOOC.

Finding Consistency in Rousseau

Several of Rousseau＇s critics begin with the presupposition that his writings are inconsistent or incoherent ann proceed to locate the ＂essence＂ of his philosophy in some of his writings while excluding others. Ernst Cassirer is among the few philosophers who have attempted to defend Rousseau＇s claim to consistency. Despite its broad influence, Cassirer＇s interpretation has remained largely unchallenged. The aim of this paper is twofold. Firstly, it aims to show that Cassirer＇s interpretation undermines （1） the important role Rousseau assigns to pity in both the state of nature and civil society and （2） the significant role the general will plays in his political theory. Secondly, it proposes an alternative interpretation that succeeds in uniting Rousseau＇s opus.

A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

A hybrid algorithm to design the multi layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial immunology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore improves the probabiligy of training algorithm to converge to global optima. The applications of the neural network in the modulation style recognition of analog modulated rader signals demonstrate the good performance of the network.

Media Effects on Children＇s Social and Moral Development： A Theological Moral Study in Africa

In this 21 st century, whether we like it or not, it is very clear that the Intemet, social media, and all ad of the related technology have come to stay. This new technological discovery has brought great and wonderful benefits to our family＇s lives and relationships. However as it is always the case with any new innovations, it has its own negative side. This paper aims at investigating what actually social media is and how it translates to children, especially African children, as it relates to family and social living. Actually Internet provide benefits in areas such as cognitive and social development, it also presents risks together with opportunities for children＇s growth and development. It is common today to see children spending great quality of their time using other social websites. It is also evidenced that Interact provide benefits in the area of emotional and intellectual development and at the same time present risk as well as opportunities to children＇s development in functional and societal challenges. Therefore social media gives the children of the present age the opportunity to enter into global entertainment and communication. As a result of this development, there is now Internet addiction of children. Hence, there is serious need for parents to be aware of the nature of social media sites, their children are using. This is because not all of them are healthy environments for children. Experts such as childhood educators and psychologist even Paediatricians are equipped and so are better hands to assist parents not only to know and understand these websites but to encourage proper use and make them to monitor for potential problems such as cyber-bullying, facebook depression, exposure to inappropriate content and sexting. This paper proffers some recommendation that certainly will assist parents to bring up their children up to be vibrant adult even in this technological era.

Application of Genetic Algorithms to Optimize Neural Networks for Selected Tribological Tests

This paper presents a method of determining the friction coefficient in metal forming using multilayer artificial neural networks based on experimental data obtained from strip drawing test. The number of input variables of the artificial neural network has been optimized using genetic algorithm. This process is based on surface parameters of the sheet and dies, sheet material parameters and clamping force as input parameters to train the neural network. In addition to demonstrating the fact that regression statistics model using genetic selection and intelligent problem solver are better than models without preprocessing of input data, the sensitivity analysis of the input variables has been conducted. This avoids the time-consuming testing of neurons in finding the best network architecture. The obtained results from this study have also pointed out that genetic algorithm can successfully be applied to optimize the training set and the outputs agree with experimental results. This allows reduction or elimination of expensive experimental tests to determine friction coefficient value.

Influence and Countermeasures of Network to psychology of juvenile delinquency

With the majority of young people increasingly becoming the main force of the use of the network, the network as a new kind of social factors on the growth of young people is increasingly prominent. Network not only help young people broaden their horizons, accept avant-garde concept, concern about the world cutting-edge issues, but also spread some violence, pornographic or reactionary spare, as well as network addiction problem which will cause a serious negative impact on the healthy growth of young people and lead to increasing juvenile crime. The number of juvenile criminals has increased every year in China and has trended to lowering ages, regiment turn and malignant development, which will cause great harm to social stability. Of course, there are many causes of juvenile delinquency, but showing characteristics are also diverse.