煤矿安全管理“自学习”体系建设分析

本文就煤矿安全管理“自学习”体系的构建展开论述,通过对于自学习体系的探究,分析了在操作、预警、管理员权限、奖惩真实性等方面,现阶段煤矿安全管理中存在的问题,并以此通过实时跟踪处理系统、异常行为检测系统、电子证据链、大数据分析平台、“自学习”体系平台等方面的构建,强化煤矿安全管理中的自学习能力,进而构建一个现代化的煤矿安全管理体系,提升煤矿企业的安全管理能力。

地方成人教育机构推进当地学习型社区建设的思考

成人教育与社区的发展紧密地结合在一起,主动参与并积极推进当地学习型社区建设,成为地方成人教育机构的新使命.地方成人教育机构推进当地学习型社区建设要从我做起,为学习型社区建设提供示范和表率;主动参与,为学习型社区建设提供服务;积极协调,共同构建学习型社区的"自学习"机制.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

College English autonomous teaching and learning research and practice

This paper intends to promote a college English autonomous teaching and learning approach by introducing the whole process of its implementation and feedback from the learners. The theoretical and practical framework of this approach is： with multiple autonomous learning research and practice models as its core, with process syllabus as its guidance, with task-based teaching as its essential principle, with group cooperation and reciprocal learning as its means, with extracurricular activities, online learning and self-access center as its learning environment, with formative assessment system as its guarantee and with cultivation of learners＇ comprehensive English practical skills and autonomy as its goal. Through this approach, we provide the learners with a favorable learning environment where they can learn by themselves and learn by reflection and practice so that they can learn how to learn and how to behave and how to survive.

Meta-cognitive Strategy Training and the Development of Learners＇ Autonomy in Language Learning

As the ultimate goal of education, autonomy in language learning has aroused a lot of attention from scholars at home and abroad. While in universities of China, students do not have strong autonomy in English language learning. The author tries to adopt specific meta-cognitive strategies to facilitate students＇ autonomy in learning by improving learners＇ capacities in study planning or management, monitoring and evaluating in learning to raise their consciousness and ability in autonomy, and lay a foundation for life-long learning.

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

INTELLIGENT PRECISION CONTROL IN CNC GRINDING

The development of intelligent control techniques provides powerful means for the control of machine tools. In this paper, a intelligent control technique and an algorithm for precision control of CNC grinding of ceramic chips are introduced. In the process of ceramic chip CNC grinding, the dimension of the chips tends to get larger and the dimensional error to exceed the tolerance as the number of the chips increases which are machined on the same part program. There are many factors leading to the occurrence of the error and the law of error variation is very complicated. With the introduced intelligent self learning error compensation technique, the CNC system can improve the control strategy to compensate the error automatically. The simulational result is also given.

On fostering the autonomous learning ability in college English teaching

Autonomous learning is one of the objectives of multi-media college English teaching. On basis of the test of students＇ autonomous learning ability and the analysis of the results, this paper attempts to explore the feasibility of fostering the autonomous learning ability in college English teaching.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

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.

Research into college students＇ English autonomous learning capability and the necessity of cultivating metacognitive strategies

Through the research into college students＇ English autonomous learning ability of the non-English major students. That the cause why university students＇ English autonomous learning ability is weak is proved to be that they do not value the use of learning strategies. The use of learning strategies can promote the formation and enhancement of autonomous learning ability of the learners. Metacognitive strategy is a high-level management skill which can enable the learners to plan, regulate, monitor and evaluate actively their own learning process. Massive researches have proved whether metacognitive strategy is used successfully or not can directly affect the student learning result. So, it is necessary for teachers to cultivate and train the students to use metacogitive strategy in the university English teaching.

Adaptive topology learning of camera network across non-overlapping views

An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.

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The Application of Constructivism Theory to the Student-determined College English Learning

According to the further exploration into constructivism theory, the author illustrates the application of this theory to China＇s college English teaching, especially in the new perspective of student-determined learning.

Nominal friction coefficient in spread formulas based on lead rolling experiments

Friction coefficients in spread formulas were studied under low width-to-thickness ratio. The effects of all the factors on friction were considered as different roughness of surfaces. After lead rolling experiments in 5 different roughness grades, friction coefficients were obtained. With changing width-to-thickness ratio, reduction rate and ratio of diameter of roller to thickness, all the nominal friction coefficients which can be used in these formulas were calculated. Then, a fitting expression was proposed, comparing with the results measured in 232 times tests, the errors of the nominal friction coefficients calculated by the expression are mostly less than 12%. After a certain times self-learning, the errors are no more than 2%. With the varying nominal friction coefficients, the spread will be predicted more accurately. When the nominal friction coefficient is used to predict the spread under the real working condition, the results calculated are also in agreement with the measured ones, and the errors are less than 2%. This credible reference and solution about how to set the friction coefficient in spread formulas would also be used in practical industrial production.

Avoidant Learning Ability in Free Flying Housefly (Aldrichina grahami) by Electric Shock

Previous studies have confirmed that both honeybee and Drosophila are capable of learning and memory. This study aimed to investigate whether the house fly （Aldrichina grahami）, with strong instincts to adapt their living environment, have the learning ability to associate odor stimulus to avoid electric shock in free flying state using a device developed by the authors. The result showed the learning ability ofA. grahami at the electric shock voltages of 5 V, 25 V and 45 V AC. When 60 V was used, the flies were frequently injured. Our results indicate that A. grahami is a good model to study the neural mechanism of learning and memory. The paradigm in this study has some advantages that can be used in future studies of free insects.

A novel self-organizing E-Learner community model with award and exchange mechanisms

How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.

Fast Learning in Spiking Neural Networks by Learning Rate Adaptation

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.