隐蔽老火区立体式综合治理研究

对2320隐蔽老火区首先通过灌注泥浆对火区进行治理,同时对部分裂隙通道进行堵漏,然后对泥浆不能到达的区域注入高倍数三相泡沫灭火材料泡沫,瞬间将火源处覆盖,火源因隔绝氧气而窒息灭火。三相泡沫具有良好的堆积性和流动性,对煤体具有保水保湿,惰化遗煤氧化空间,并能够对松散煤体进行化学阻化,从本质上降低遗煤自然氧化速率,消灭了隐蔽老火区的火灾隐患,具有较高的推广价值。     

石油化工生产火灾危险性分析

针对石油化工生产的特点,从物料特性、上艺装置、工艺控制参数、防火防爆安全装置、作业性质和消防安全设施等几方面综合分析其火灾危险性,并提出相应的防范措施,为消防安全评价提供依据。     

一个新颖的轮廓线跟踪算法

提出了一个新颖的基于隐马尔科夫模型与光流的轮廓线跟踪算法。曲线描绘由B样条形状空间向量来表达,能够捕捉全局和局部变形。提出应用沿曲线的光流计算来预测曲线在下一帧的位置,在预测曲线的基础上,提出应用隐马尔科夫模型来准确定位曲线的位置。隐马尔科夫模型提供了一种有效的概率手段来融合多种量测特征比如边缘,曲线平滑性,区域灰度或颜色统计信息等等,能够更准确的定位曲线位置。基于仿射形状空间的实验了表明本文所提出算法的有效性。     

抗洪抢险措施及效果

结合“９８”抗洪抢险实例，对多种险情作了具体分析，通过汛后调查，对抗洪抢险消险措施和效果进行了总结，为今后进一步做好防洪消险工作提供了经验。     

Learning to Perceive and Act by Trial and Error

This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon perceptual aliasingand show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.     

安徽省长江干堤的几种加固措施

安徽省境内765km的长江干堤，由于均是在原来旧堤的基础上经逐年加高培厚的，修筑时均未按设计要求施工，致使堤身密实度不够，新老堤土之间结合不牢，在汛期高水位时易产生渗漏。20世纪70年代以来，安徽省高度重视对长江干堤渗漏隐患的处理，经过长期的实践，逐渐摸索出一套行之有效的除险加固方法，如锥探灌浆、灭杀白蚁、冲抓套吉、垂直铺塑防渗、粘土铺盖及压渗盖重等处理措施。使长江干堤的渗漏隐患得以基本消除，其具体作法可供类似工程借鉴。     

一起500kV线路计量点事故隐患的分析与思考

本文简要介绍了500kV某回线计量点验收时发现事故隐患的过程,从电容式电压互感器本身结构出发,详细分析了产生事故隐患的原因、带来的危害等,并由此提出了计量点验收工作的重要性和必要性。     

无端点检测汉语识别算法的实现及改进——动态时间规整和隐马尔可夫统一模型的应用

语音识别算法中，动态时间规整（ＤＴＷ）和隐马尔可夫模型（ＨＭＭ）是最有效的识别算法，并且两者之间有着本质的联系和内在的统一［１］，据此前期工作中，已经建立了ＤＴＷ和ＨＭＭ的统一模型（ＤＨＵＭ）［２、３］。本文对ＤＨＵＭ进行了改进，在ＤＨＵＭ中引进寂静段自环，并根据汉语语音的特点，提出了一种无端点检测的语音识别算法。在识别过程中，该算法无需确定语音信号起止点位置，而是从寂静段开始，直接按帧提取特征（帧长２０ｍｓ，帧间重叠５０％），特征向量由１５阶倒谱系数和帧平均能量组成。实验中，用ＤＨＵＭ实现了该算法，对９９个相似汉语单字的识别测试结果表明：无端点检测的识别正识率为９４．９５％，正识率下降很少，但不作端点检测却降低了算法的复杂程度。为进一步改善识别性能，特征向量采用一种听觉模型特征，识别器具有更好的鲁棒性，识别率会略有提高。     

Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.