Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.     

An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works

An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.     

企业并购的商誉问题

企业兼并重组是当前资本市场引人注目的热点，在兼并中无一例外涉及到商誉问题。但是我国对商誉会计的处理存在问题，如商誉摊销对企业的利润指标产生负面影响，商誉创造超额收益的能力不随价值摊销而消失等。对购并中的商誉问题进行探讨：首先阐述了企业并购中商誉涵义的理论本质、构成要素；其次分析了在企业并购中怎样对商誉进行初始确认和计量；最后论述了企业并购后对商誉的会计处理以及对负商誉的认识。同时提出了规范我国商誉会计处理的一些建议。     

阳离子絮凝剂P(DMC-AM)的合成及其絮凝性能

采用氧化还原剂和偶氮化合物组成的复合引发体系,合成了阳离子絮凝剂 P(DMC-AM)。探讨了反应体系 pH 值、单体浓度、阳离子度、反应时间、烘干温度等因素对产物特性粘数的影响。较佳工艺条件为:反应体系 pH=4.5、单体浓度40%、阳离子度40%、反应时间2.5 h、烘干温度60℃,产物的特性粘数为13.4 dL/g。在污水处理实验中,经其处理过的污泥的透光率、脱水率等指标均与国外同类产品的絮凝效果相当。     

空时联合均衡水声数据检测仿真研究

水声信道的衰落特性对单传感器数据检测性能影响较大。本文采用空间分集技术，结合判决反馈均衡器，即进行空时联合均衡，实现了水声多径衰落信道的数据检测仿真，分析了检测性能，并给出了均衡算法参数的选取方法。仿真结果表明，当信道出现衰落时，单通道数据检测效果较差，而通过空时联合均衡可以使检测误码率显著降低，提高了水声传输系统的可靠性。     

抗真菌乳酸菌的筛选鉴定及发酵特性

利用琼脂扩散法,对60株从传统乳酸菌发酵食品分离出来的乳酸菌进行抗真菌特性的筛选,得到一株抑菌性能较好的乳酸菌菌株AST18,经16s rDNA鉴定为干酪乳杆菌Lactobacillus casei,并进一步对AST18发酵特性进行研究,研究表明AST18最适生长温度为37℃,培养基最适初始pH值为6.5,培养48 h以后抑菌物质生成量达到稳定.     

低脂再制和模拟干酪中脂肪替代物——麦芽糖醇的应用研究

目的:研究麦芽糖醇在低脂干酪中作为脂肪替代物的应用潜力,探究其对低脂再制马苏里拉干酪和低脂模拟干酪的功能特性及微观结构的影响。方法:通过测定由不同含量的麦芽糖醇(0%,1%,2%,3%)制成的低脂再制马苏里拉干酪和低脂模拟干酪的基本组分、质构特性、功能特性、微观结构及疏水作用力,研究不同添加量麦芽糖醇对上述干酪的影响。结论:麦芽糖醇作为脂肪替代物效果较好,能够提高酪蛋白分子间疏水作用力,降低干酪硬度,改善低脂干酪的功能特性。本研究表明麦芽糖醇可作为一种良好的脂肪替代物应用于低脂干酪。     

基于MDL比值的战场目标个数盲估计算法

由于基于MDL准则的信源数估计算法只适用于估计白噪声条件下非相关源的数目,为了适应战场的实际应用环境,提出了基于改进MDL准则的MDL比值信源数目估计算法,有效地解决了基于传统的MDL准则算法在有色噪声条件下信源数估计失效的问题。为了研究空间噪声相关性对所提算法的影响,建立和推导出了适用于圆阵的空间相关有色噪声模型。通过仿真实验和实测数据的验证试验,验证了所提出算法的有效性和可行性。     

基于物联网的信息感知与交互技术应用研究

随着全球经济发展速度的加快,世界各国对物联网的信息化发展提出了全新的要求。物联网对国家信息化发展与管理具有很好的推动作用,是信息网络未来发展的主要平台。本文中作者分析了物联网中信息感知与交互技术的使用概况与具体构成,对物联网的应用展开了分析。