运输包装黑匣子的研究

运输包装损坏主要由流通过程中装卸、运输、存储等环节以及包装物设计本身所造成。介绍了能对上述损坏因素进行全过程监测的黑匣子。通过黑匣子对流通过程中包装件的加速度、温度、湿度进行监测并记录在存储器中,然后,通过对黑匣子记录数据的读取,取得损害原因数据,从而确定损坏原因。     

联合收获机谷物状态监测装置采样盒及其减振器

联合收获机的作业参数与作业性能密切相关,为了满足联合收获机作业性能的实时监测需求,设计了谷物状态监测装置采样盒及其减振器。采用动力学原理分析了联合收获机作业过程中阻流阀往复运动的“采样-卸样”动作特性;运用能量守恒原理建立了有约束条件的减振器保守系统动力学模型;利用多体动力学分析软件ADAMS建立了减振器动力学模型的虚拟样机并进行了求解,通过田间试验验证了该装置的可行性。对比无减振器作业情况,有减振器时谷物状态监测装置获取的图像中籽粒和杂质边缘清晰,图像质量更高;在同样的图像处理算法下,有减振器时破碎率监测结果精度提升了13.7%,监测精度达到了81.3%,含杂率监测结果精度提升了33.6%,精度达到了86.0%。     

高强钢焊接箱形截面压弯构件局部稳定试验研究

为研究高强钢压弯构件的局部稳定性能，对5个Q460C和5个Q690D钢焊接箱形截面构件进行了单向偏压试验，分析了其破坏形式、局部稳定性能以及承载力；将实测承载力与欧洲规范EN 1993-1、我国标准GB 50017—2017和美国规范ANSI/AISC 360-16相关公式计算结果相比较，以验证各规范对Q460C钢和Q690D钢焊接箱形截面压弯构件屈曲后强度计算的适用性。研究结果表明：所有高强钢焊接箱形截面压弯构件均在柱中附近发生局部屈曲破坏；由轴向压力-轴向压缩变形或轴向压力-水平位移曲线可知，其为极值点失稳；构件的轴向压力-水平位移或轴向压力-应变曲线的形状和局部屈曲模式有关；在翼缘宽厚比为28.1～56.3、腹板高厚比为40.2～80.4、偏心距为20～50 mm范围之内，EN 1993-1和GB 50017—2017中的屈曲后强度计算公式仍然适用于Q460C和Q690D钢焊接箱形截面压弯构件，而ANSI/AISC 360-16中的相关公式需要进一步修正。     

预应力现浇箱梁施工工艺及质量控制探讨

现浇箱梁有很多优点，但由于预应力现浇箱梁属于超静定结构，造成和简支梁不一样的受力特点，其施工工艺较复杂，技术要求比较高。本文重点对预应力现浇梁施工的工艺和在施工过程中质量控制做进一步的讨论。     

黄祁高速上跨皖赣铁路40 m预应力组合箱梁架设方案

伴随现代化建设步伐的加快，我国铁路、公路网的密度也越来越高。而直接导致的结果是，交通方式穿插出现的方式越来越多，交通道路的资源也越来越紧张。本文采取实例，分析了高速公路中，跨铁路预制箱梁的架设方案，以供同行参考。     

标准段预应力混凝土箱梁浇筑的控制方法

通过对武汉市黄浦大街-金桥大道快速通道工程在标准段预应力混凝土箱梁浇筑顺序、浇筑方法、人员配置等方面的经验总结,同时简要介绍了在标准段预应力箱梁在浇筑过程中应该注意的事项和措施,为今后类似桥梁工程预应力混凝土箱梁浇筑提供参考借鉴。     

谈配电箱壳体与暗配管同时预埋施工

对配电箱壳体与暗配管同时预埋施工的特点进行了阐述,介绍了该施工方法的适用范围及工艺流程,并分析了施工过程中应注意的操作要点,基于相关规范标准,研究了贯穿施工全过程的技术管理措施,使企业获得了良好的经济效益。     

预应力小箱梁水文计算分析

结合滦河镇大桥工程概况,简述了桥梁截面形式及截面尺寸,根据设计流量进行了小箱梁水文计算,包括冲刷计算及桥孔长度计算,从而为确定桥梁跨径是否合理提供了参考。     

Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine

Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).     

寒区PC连续箱梁日照温度场测试与分析

对黑龙江省齐齐哈尔嫩江大桥PC连续箱梁日照温度场进行测试,分析箱梁顶板、底板、腹板温度变化规律及温差分布规律,总结得出计算箱内温度的经验公式。