酸轧机组薄带无套筒卷取工艺研究与应用

针对0.35 mm规格无取向中低牌号硅钢薄带套筒卷取无法保证连续批量生产及卷取质量的问题，对其卷取工艺进行了分析与研究。结果表明，卷取张力、张力锥系数、张力锥作用卷径系数以及皮带助卷器压力是其主要影响因素。通过卷取参数的优化试验，确定了0.35 mm规格无取向中低牌号硅钢薄带的无套筒卷取最优工艺参数，下线钢卷质量良好。薄带无套筒卷取工艺在酸轧机组的应用，不仅提高了钢卷内圈质量，还降低了套筒的周转与消耗，从而降低了无取向硅钢的生产成本。     

水下粉细砂土体在超高频机械振动荷载作用下液化特性的试验研究

为了量化高频机械振动对土壤液化的具体效果,在试验室泥槽中对不同性质的水下粉细砂沉积物开展了诸如不同振动作用时间及不同工况下的液化试验,利用静力触探仪测量了振动作用前后其承载力变化情况,基于试验数据分析讨论了振动作用时间、振动作用距离、振动频率对于液化效果的影响.     

利用CPTU对淤泥质黏土工程性质的评价

针对某场地河滩沉积淤泥质黏土,利用CPTU(孔压静力触探)对该场地不同深度的淤泥质黏土进行原位孔压消散试验,得出其固结系数。将该固结系数与室内试验所得的固结系数进行比较,得到现场测得的固结系数比室内试验测得的固结系数约大1～2个数量级的结论。介绍了CPTU估算不排水抗剪强度的方法,对比十字板剪切试验结果,对各种估算方法在该种土质中的适用性作出评价,确定了各种方法经验系数的取值范围。     

圆锥裁剪BLOB的视频目标检测方法

真实场景的视频目标检测需要消除阴影、反射和鬼影等噪声的影响,以检测出运动目标和静止目标.为了实现系统性的视频目标检测,提出一种自适应圆锥裁剪联通块(TC-BLOB)榆测方法.基于BLOB知识,将3D颜色空间变换为"夹角-模差"2D空间后,定义一套圆锥裁剪规则划分出阴影BLOB和反射BLOB;再以一种持久化记忆PM方法判别出鬼影BLOB;最后改进双背景模型检测出静止目标和运动目标.采用不同环境视频进行实验的结果表明,文中方法足有效的,并有独立于后续跟踪的优点.     

考虑循环寿命折损的主动配电网仿射可调鲁棒优化方法

针对主动配电网在源荷储协同下的不确定性优化问题,提出了基于仿射可调鲁棒优化的主动配电网运行策略。首先,考虑分布式光伏的可控模型、循环寿命折损的电池储能模型和ZIP负荷模型,构建了主动配电网优化运行模型。然后,采用椭球集合来描述分布式光伏出力的不确定性,并结合仿射策略形成优化模型中决策变量与分布式光伏出力不确定性变量之间的线性决策机制。最后,通过对偶变换将主动配电网鲁棒优化模型转化为一个确定性的混合整数二阶锥规划模型进行求解。在改进的IEEE33节点算例系统中对比验证了所提出的仿射可调鲁棒优化方法的有效性和优点。     

考虑电池储能老化和需求侧响应的交直流混合配电网动态重构方法

现有的电池储能模型一方面认为电池的充放电功率和效率为固定不变的,另一方面有关电池储能的充放电过程对循环老化的影响缺少考虑,这与电池单元本体的非线性等效电路模型相违背。因此,考虑电池储能的循环老化成本、需求侧响应,提出了交直流混合配电网动态重构方法。通过对实际电池储能的充放电功率的运行区域进行采样,在凸包络的采样点集合中将非理想状态下的电池储能模型重构为线性模型。在此基础上,分别建立了基于累积吞吐电量和放电深度的循环老化成本模型,构建了综合成本最小化的交直流混合配电网动态重构模型。并采用混合整数二阶锥规划方法求解。在改进的33节点算例上仿真验证了所提基于放电深度的循环老化成本模型的经济性,分析了交直流混合配电网重构方法经济性的影响因素。     

基于机会约束规划的含电动汽车主动配电网能量管理方法

针对含电动汽车(EV)和分布式光伏的主动配电网(ADN),提出了一种基于机会约束规划的能量管理方法。首先,基于EV用户的出行特性和需求,构建了基于分段线性化的EV智能充放电决策模型;其次,通过支路潮流模型与二阶锥松弛,构建了含EV与分布式光伏的ADN一体化数学模型,EV集群作为灵活可控单元主动参与ADN的能量管理;然后,为了充分考虑分布式光伏的不确定性,采用机会约束规划方法描述了含不确定性参数的数学模型,并对模型中的机会约束条件进行确定性转换;最后,考虑不同城市功能区内含EV与分布式光伏的ADN场景,进一步对比分析了不同的机会约束条件置信水平下EV对ADN经济安全运行的影响。仿真结果表明,适度降低机会约束条件的置信水平能在保证EV用户出行需求的同时充分地挖掘EV充放电行为的灵活性,进一步实现ADN的灵活运行。     

一种适用锥头体的嵌入式大气数据传感系统改进校准算法

嵌入式大气数据传感系统的空气动力学模型基于钝头体推导,该模型是否适用于锥头体尚未得到证实;对一典型锥头体进行了空气动力学模型适用性验证,验证结果表明该空气动力学模型用于锥头体时动静压相对误差超过了2.5%;对此,提出了一种适用于锥头体的改进校准算法,并且进行了仿真验证;仿真结果表明动静压相对误差小于0.5%,改进的校准算法有效消除了模型误差。     

Inner approximations of completely positive reformulations of mixed binary quadratic programs: a unified analysis

Every quadratic programming problem with a mix of continuous and binary variables can be equivalently reformulated as a completely positive optimization problem, that is, a linear optimization problem over the convex but computationally intractable cone of completely positive matrices. In this paper, we focus on general inner approximations of the cone of completely positive matrices on instances of completely positive optimization problems that arise from the reformulation of mixed binary quadratic programming problems. We provide a characterization of the feasibility of such an inner approximation as well as the optimal value of a feasible inner approximation. In particular, our results imply that polyhedral inner approximations are equivalent to a finite discretization of the feasible region of the original completely positive optimization problem. Our characterization yields, as a byproduct, an upper bound on the gap between the optimal value of an inner approximation and that of the original instance. We discuss the implications of this error bound for standard and box-constrained quadratic programs as well as general mixed binary quadratic programs with a bounded feasible region.     

Data envelopment analysis classification machine

This paper establishes the equivalent relationship between the data classification machine and the data envelopment analysis (DEA) model, and thus build up a DEA based classification machine. A data is characterized by a set of values. Without loss of the generality, it is assumed that the data with a set of smaller values is preferred. The classification is to label if a particular data belongs to a specified group according to a set of predetermined characteristic or attribute values. We treat such a data as a decision making unit (DMU) with these given attribute values as input and an artificial output of identical value 1. Then classifying a data is equivalent to testing if the DMU is in the production possibility set, called acceptance domain, constructed by a sample training data set. The proposed DEA classification machine consists of an acceptance domain and a classification function. The acceptance domain is given by an explicit system of linear inequalities. This makes the classification process computationally convenient. We then discuss the preference cone restricted classification process. The method can be applied to classifying large amount of data. Furthermore, the research finds that DEA classification machines based on different DEA models have the same format. Input-oriented and output-oriented DEA classification machines have similar properties. The method developed has great potential in practice with its computational efficiency.