弘扬长征精神加强当代大学生的理想信念教育

理想信念教育是新形势下加强和改进大学生思想政治教育的核心.当代大学生在理想信念上存在着政治信仰迷茫、理想信念迷糊,价值取向重功利、讲实际等问题.高校在加强大学生的理想信念教育过程中,应充分依托和利用长征精神的宝贵思想资源,发挥思想政治理论课的主渠道作用,把长征精神的理论成果贯穿教学过程中;以社会实践活动为载体,升华大学...     

Reliability Analysis of Excavator Rectifier Feedback System with Multi-State Components Based on Belief Universal Generating Function Method

In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characterize the uncertainty caused by various factors. A modified Markov model is proposed to obtain the state probabilities of components at any given moment and subsequently the mass function is used to represent the precise belief degree of state probabilities. Based on the primary studies of universal generating function(UGF)method, a belief UGF(BUGF) method is utilized to analyze the reliability and the uncertainty of excavator rectifier feedback system. This paper provides an available method to evaluate the reliability of multi-state systems(MSSs) with interval state performances and state probabilities, and also avoid the interval expansion problem.     

建立在科学分析的理性基础之上的理想信念论述

社会主义是一个从工人阶级眼前的现实利益出发的、并代表着其长远利益的革命运动,当前所采取的一切经济的和政治的制度和方法,都是为实现运动未来的理想而必须采用的过渡措施.     

基于置信规则库的驾驶人愤怒情绪识别模型

针对“路怒症”诱发的攻击性驾驶行为影响交通安全问题,提出一种基于生理与脑电特征的驾驶人愤怒情绪识别模型.招募15 名被试在武汉市区开展限时实车试验.由于所选实验线路为交通繁忙路段,被试易被行人横穿马路、车辆加塞等道路事件诱发愤怒情绪.实验中采用愤怒量表记录被试的愤怒等级,以及采用生物反馈仪与脑电记录仪记录被试的生理和脑电信号.将血容量脉冲、皮肤电导、δ 波百分比与β 波百分比这四项生理与脑电指标作为模型输入,建立基于置信规则库的愤怒情绪识别模型.验证结果表明,该模型的识别精度(82.24%)较BP神经网络、SVM分别提高7.15%、5.02%.研究结果可为开发基于生理和脑电信号的驾驶情绪识别设备提供理论支持.     

加强学生思想政治教育时效性的研究

中国共产党十八届三中全会召开之后,本文作者遵照学校党委相关要求,认真回顾和审视了过去一段时间的学生思想教育工作,以实践的标准,肯定了成功的做法,总结了一些工作经验,发现了差距和不足。本文的着力点主要在于,围绕学生思想教育这个老课题,在思想上力求创新,在内容上力求创新,在方法上力求创新,努力回答在实践中如何把思想认识从那些不合时宜的观念、做法和体制的束缚中解放出来,从对马克思主义的错误的和教条式的理解中解放出来,从主观主义和形而上学的桎梏中解放出来等问题,努力使发扬革命传统和创造新鲜经验能够有机结合起来,以促进学生思想教育工作的自觉性和科学性,开创学生思想教育工作的新局面。     

基于扩展置信规则库的海盗袭击事件风险预测

为有效应对海盗袭击事件，减少可能产生的损失，本文提出一种新的基于扩展置信规则库 (EBRB)联合优化的海盗袭击事件风险预测模型。通过引入Relief F算法和差分进化算法，从结构和参数两个角度对EBRB系统进行优化，以确保EBRB系统具有最优的参数数量和取值，利用实际海盗事件数据集进行模型验证。结果显示，联合优化的EBRB系统预测结果与实际情况的拟合效果较好，相对于初始的EBRB系统，联合优化EBRB系统将海盗事件的风险预测准确性提高 了60%。此外，与现有其他预测模型对比发现，基于联合优化EBRB系统的预测模型在提高预测准确性方面具有一定的优势。     

Methods using belief functions to manage imperfect information concerning events on the road in VANETs

Different models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab™ simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example.     

Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

ABSTRACT

In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on an improved deep belief network (DBN) classification algorithm is proposed. An improved method of input is constructed using a Restricted Bolzmann Machine (RBM) with T distribution function visible layer nodes, which can convert information on pedestrian features to a Bernoulli distribution, and the Bernoulli distribution can then be used for recognition. In addition, a middle layer of the RBM structure is created, which achieves data transfer between the hidden layer structure and keeps the key information. Finally, the cost-sensitive Support Vector Machine (SVM) classifier is used for the output of the classifier, which could address the class-imbalance problem. Extensive experiments show that the improved DBN pedestrian detection method is better than other shallow classic algorithms, and the proposed method is effective and sufficiently feasible for pedestrian detection in complex urban environments.     

论法律信仰的培养

本文从法律信仰的涵义入手,在分析我国法律信仰缺失的原因基础上,探讨如何培育社会主体的法律信仰,建设社会主义法治国家。