学习型离退休党支部建设探析

建设学习型离退休党组织,对加强离退休党支部建设,提升党组织凝聚力和战斗力具有重要意义。建设学习型离退休党支部是推动离退休党建工作科学开展的需要,是发挥离退休党员干部参谋助手作用的需要,是提高离退休党员干部生活质量的需要。建设学习型离退休党支部,必须把理论武装工作放在首位,必须服务于党的中心工作,必须立足于离退休党员干部素质的提高。我们要努力营造建设学习型离退休党支部的浓厚氛围;强化建设学习型离退休党支部的制度保障;采取确保学习效果的多种学习方式。     

一般分布下的自学习滤波方法

针对Ｋａｌｍａｎ滤波存在的储多难点，在一般分布下的二阶近似最小方差估计的基础上，本文首次提出了正态严平稳假设下的自学习自适应Ｋａｌｍａｎ滤波，并相应研究了分布蜜度的离线异步自学习估计方法，文中又利用了Ｂａｙｅｓ公式得到了非线性校正项的三阶联合中心矩和四阶中心矩的自学习估计方法，并据此提出了一般分布下的自学习自适应滤波的基本公式。本文对上述理论的收敛性进行了严格证明，并指出了待研究的若干问题。     

《材料合成与制备技术》课程线上线下混合教学模式探讨

《材料合成与制备技术》是材料化学专业重要的专业课程。课程开设是为了使学生掌握材料合成与制备的基本原理和相关技术,课程教学以老师讲授为主的传统教学形式进行。为了进一步提高教学效率,提升教学效果,结合现阶段网络教育的高速发展,采用线上线下混合教学模式,鼓励学生参与到课程教学过程。经过教学实践,线上线下混合教学模式在增加师生联动,丰富考核机制等方面优势突出,教学效果良好,学生学习成绩提高明显。     

Learning control of fermentation process with an improved DHP algorithm☆

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system. However, ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cel mass, substrate, feed-rate, etc. An improved dual heuristic program-ming algorithm based on the least squares temporal difference with gradient correction (LSTDC) algorithm (LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process. As a new algorithm of adaptive critic designs, LSTDC-DHP is used to realize online learning control of chemical dynamical plants, where LSTDC is commonly employed to approximate the value functions. Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces. Simulation results demonstrate the effectiveness of LSTDC-DHP, and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.     

基于信息化管理的实践教学体系构建

本文首先阐述了实践教学体系的构建,并在规范化、制度化基础上,给出了信息管理系统的需求,确定了信息化管理系统的总体架构和主要功能.该系统的使用,提高了管理效率,同时具备了监督功能,保证了培养质量,取得了较好的效果.     

Team formation on the basis of Belbin’s roles to enhance students’ performance in project based learning

This paper presents a method that instructors have designed and implemented to form balanced teams based on Belbin's roles, with the aim of boosting positive interdependence and individual accountability within the teams and improving their performance in a project-based learning environment. Students’ performance has been measured through the scores obtained during the project, individual exam and Individual Accountability Factor (IAF) and compared with cohorts of previous years, in which team composition was self-selected by students. Belbin teams (18/19–19/20) have performed significantly better than self-selected teams (16/17–17/18). Additionally, students’ feedback experience and opinion has been collected. Students belonging to Belbin teams acknowledge that they attend classes more regularly, they need less time for study outside the classes and they show a higher interest for the subject at the end of the course. They also agree that working on Belbin teams has helped them to mainly improve interpersonal relationships and social skills, followed by positive interdependence and individual accountability. This team forming method gives students the opportunity to identify their own strengths and weaknesses and understand the roles (behaviours) of their teammates as well as their strengths and weaknesses. Besides, it encourages learners to focus explicitly on group work skills.     

红景天对睡眠剥夺大鼠学习记忆的影响及其机制探索

目的: 探讨红景天对睡眠剥夺大鼠学习记忆功能的影响及其可能机制。方法: 用小平台水环境法建立睡眠剥夺模型,每组半数行Y型电迷宫测试,并检测海马和额叶组织中超氧化物歧化酶(SOD)活力及丙二醛(MDA)含量,半数利用免疫组化法检测海马区胆碱乙酰转移酶(ChAT)及胶质纤维酸性蛋白(GFAP)表达。结果: Y型电迷宫测试显示,与模型组相比,红景天组全天总反应时间(TRT)和错误反应次数(EN)均较低(P<0.05)。与模型组相比,红景天组海马组织和额叶组织SOD活力均较高(P<0.05)。与模型组相比,红景天组海马组织MDA含量较低(P<0.05),额叶组织MDA含量与模型组差别无统计学意义(P>0.05)。ChAT表达检测表明,与模型组相比,红景天组阳性细胞着色较深,轮廓较清晰,排列较整齐,各亚区阳性细胞数量较多(P<0.05)。GFAP表达检测显示,与模型组相比,红景天组阳性细胞着色较浅,细胞体积较小,突起较短较细,侧枝较少,阳性细胞数量较少(P<0.05)。结论: 红景天对睡眠剥夺后大鼠脑组织氧化损伤及神经元损伤有保护作用,可缓解睡眠剥夺造成的学习记忆功能的下降。     

基于神经网络的数控机床位置控制的研究

本文提出了一种新型的神经网络结构的位置控制器，给出了该控制器的学习算法，并进行了数控机床位置伺服系统的仿真实验，结果表明这种控制器性能优越，自适应能力强。     

Dynamic interaction of attractors across multiple cortical networks as a neural basis for intersensory facilitation

I propose a neural network model for intersensory facilitation and investigate its essential neuronal mechanisms. The model consists of sensory networks (SI, SII) and an integration network (IT). The integration network binds information derived from the sensory networks and sends feedback signals to them. Through a Hebbian learning process, point attractors representing individual features and objects are created in the dynamics of the sensory networks and integration network, respectively. The ongoing state of each network is a randomly itinerant state among these point attractors. When the SI network is stimulated with a feature (I_ n ) belonging to an object (O_ n ) with suprathreshold intensity, the point attractor corresponding to I_ n emerges in the SI network, but does not when stimulated with subthreshold intensity. Intersensory facilitation occurs when associate feature II_ n derived from the other modality belonging to the same object (O_ n ) is simultaneously presented to other sensory network SII, where the point attractor corresponding to feature I_ n emerges even when the sensory networks are stimulated with subthreshold intensity. I suggest here that the dynamic interaction of relevant point attractors across multiple neural networks is essential for intersensory facilitation, and that self-organized synaptic modulation effectively contributes to intersensory facilitation when crossmodal stimuli are separated in time.     

Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.