The Coherence Cube Computing Method with Self-adaptive Time Window Based on Wavelet Transform

The coherence cube technology has become an important technology for the seismic attribute interpretation, which extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channels to identify the fault form. The coherence cube technology which uses constant time window lengths can not balance the shallow layers and the deep layers, because the frequency band of seismic data varies with time. When analyzing the shallow layers, the time window will crossover a lot of events, which will lead to weak focusing ability and failure to delineate the details. While the time window will not be long enough for analyzing deep layers, which will lead to low accuracy because the coherences near the zero points of the events are heavily influenced by noise. For solving the problem, we should make a research on the coherence cube technology with self-adaptive time window. This paper determines the sample points＆#39; time window lengths in real time by computing the instantaneous frequency bands with Wavelet Transformation, which gives a coherence computing method with the self-adaptive time window lengths. The result shows that the coherence cube technology with self-adaptive time window based on Wavelet Transformation improves the accuracy of fault identification, and supresses the noise effectively. The method combines the advantages of long time window method and short time window method.

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

Research on Refined Oil Distribution Plan Based on Dynamic Time Window

Based on the analysis of the refined oil distribution plan which includes the various vehicles models-single oil-multiple gas stations. This paper puts forward the dynamic time window of oil supplementation based on every moment, and establishes the mathematical model of the refined oil distribution plan, using C language, taking various circumstances of the model into account to find the optimal solution through several operations. This process can make the refined oil distribution plan and the distribution route is more reasonable. At the same time, the distribution cost is lowest. Through the analysis of the experimental results, the validity and algorithm of the model are proved.

Alexa Fluor 488-conjugated cholera toxin subunit B optimally labels neurons 3-7 days after injection into the rat gastrocnemius muscle

Neural tract tracing is used to study neural pathways and evaluate neuronal regeneration following nerve injuries.However,it is not always clear which tracer should be used to yield optimal results.In this study,we examined the use of Alexa Fluor 488-conjugated cholera toxin subunit B(AF488-CTB).This was injected into the gastrocnemius muscle of rats,and it was found that motor,sensory,and sympathetic neurons were labeled in the spinal ventral horn,dorsal root ganglia,and sympathetic chain,respectively.Similar results were obtained when we injected AF594-CTB into the tibialis anterior muscle.The morphology and number of neurons were evaluated at different time points following the AF488-CTB injection.It was found that labeled motor and sensory neurons could be observed 12 hours post-injection.The intensity was found to increase over time,and the morphology appeared clear and complete 3-7 days post-injection,with clearly distinguishable motor neuron axons and dendrites.However,14 days after the injection,the quality of the images decreased and the neurons appeared blurred and incomplete.Nissl and immunohistochemical staining showed that the AF488-CTB-labeled neurons retained normal neurochemical and morphological features,and the surrounding microglia were also found to be unaltered.Overall,these results imply that the cholera toxin subunit B,whether unconjugated or conjugated with Alexa Fluor,is effective for retrograde tracing in muscular tissues and that it would also be suitable for evaluating the regeneration or degeneration of injured nerves.

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

面向复杂物流配送场景的车辆路径规划多任务辅助进化算法

在现代社会中,复杂物流配送场景的车辆路径规划问题(Vehicle routing problem,VRP)一般带有时间窗约束且需要提供同时取送货的服务.这种复杂物流配送场景的车辆路径规划问题是NP-难问题.当其规模逐渐增大时,一般的数学规划方法难以求解,通常使用启发式方法在限定时间内求得较优解.然而,传统的启发式方法从原大规模问题直接开始搜索,无法利用先前相关的优化知识,导致收敛速度较慢.因此,提出面向复杂物流配送场景的车辆路径规划多任务辅助进化算法(Multitask-based assisted evolutionary algorithm,MBEA),通过使用迁移优化方法加快算法收敛速度,其主要思想是通过构造多个简单且相似的子任务用于辅助优化原大规模问题.首先从原大规模问题中随机选择一部分客户订单用于构建多个不同的相似优化子任务,然后使用进化多任务(Evolutional multitasking,EMT)方法用于生成原大规模问题和优化子任务的候选解.由于优化子任务相对简单且与原大规模问题相似,其搜索得到的路径特征可以通过任务之间的知识迁移辅助优化原大规模问题,从而加快其求解速度.最后,提出的算法在京东物流公司快递取送货数据集上进行验证,其路径规划效果优于当前最新提出的路径规划算法.

基于GT-PowerDOE的柴油机性能优化

为研究进排气门正时角度和进气歧管长度对于柴油机综合性能的影响,利用发动机仿真软件GT-Power对某款国产柴油发电机的柴油机部分进行建模,并基于台架实验验证其模型准确性。在额定转速为1500 r/min、负载为90%的工况下,应用实验设计(design of experiments,DOE)工具,以发动机进排气门正时角度和进气歧管长度作为自变量,柴油机功率、比油耗和碳烟(soot)排放率为因变量,进行了最大功率、最小比油耗和最低碳烟排放量的多目标优化。结果表明:适当缩短进气歧管长度可以提高发动机进气压力和进气效率,提高发动机动力性和燃油经济性,增大气门重叠角可以有效降低气缸的泵气损失,提升柴油机做功效率,同时降低气缸内温度,从而降低碳烟的排放率。最终,各个目标值分别实现了1.3%、1.0%和2.6%的优化。

K-S变换及其电网超谐波时频分析应用

依据FFT→优化窗→IFFT思路,突破线性时频变换的窗函数积分性能桎梏,实现高性能优化窗函数的线性时频变换应用,建立新型时频变换算法——K-S变换.对信号x(t)的FFT频谱向量进行频移处理后,与该频移点下Kaiser优化窗的频谱向量进行Hadamard乘积,再将乘积结果进行FFT逆变换(IFFT),构造出K-S变换复时频矩阵,由此获得x(t)的时间-频率-幅值、时间-频率-相位三维信息;给出逆变换的数学推导与局部性质、线性性质和变分辨率特性;0~150 kHz电网的稳态与时变超谐波信号仿真实验表明,K-S变换的时域、频域分辨能力均优于流行的短时傅里叶变换、S变换,具有优良的变分辨率性能;0~40 kHz超谐波信号的实测证明,基于K-S变换的超谐波电压幅值测量绝对误差均小于0.032 3 V.

离散时间的完全服务并行优化轮询排队系统特性分析

轮询是一种依次有序服务的系统资源动态调度机制.针对服务器在站点间查询、服务和转移过程中的流水线作业方式导致了系统整体服务效率较低的问题,本文提出了完全服务的并行优化轮询系统.首先,构建了系统的单服务器多队列排队模型和相应的系统状态方程,并精确解析出系统特性参数的完整数学解析表达式.此外,还提出了一种系统状态稳定性的判定方法,对不同负载状态下的系统稳定性进行了定量分析.计算机仿真的统计分析结果与理论计算值相一致.最后,系统性能分析表明,在保持周期性、无冲突服务的基础上,系统的队长、时延特性和稳定状态下负载能力均得到了较大的提高.

时间窗约束下农产品物流配送路径优化研究

农产品需求量增加对物流配送提出较高挑战,基于此提出时间窗约束下农产品物流配送路径优化方法研究。依客户预期服务时间需求,取混合时间窗约束函数确定时间窗、物流配送车辆最大载重、配送路径长度与物流配送车辆约束条件,构建农产品物流配送路径优化模型;基于农产品物流配送需求改进传统遗传算法,求解构建模型,即获农产品物流配送路径优化结果。实验结果显示:相较生鲜农产品多车型冷链物流车辆路径优化,所提方法最优农产品物流配送路径获取迭代次数更少、配送路径长度更短、总成本更低,应用性能更佳。

基于改进哈里斯鹰优化算法的动态路径规划研究

针对传统栅格地图下的路径规划算法存在多峰值优化、无法实时避障等问题,提出了一种基于改进哈里斯鹰优化算法的动态路径规划方法。首先,提出方形邻格邻近扩散方法初始化哈里斯鹰种群位置,在路径规划问题模型下增加种群多样性;然后,提出一种非线性能量因子优化算法在搜索和开发之间的更新比例,提高全局搜索性能;最后,引入动态窗口法提高机器人实际运行路径的平滑程度,构造结合全局路径的动态窗口评价函数以改善动态窗口法前瞻性不足的问题。实验结果表明,所提方法可以兼顾实时避障和路径最优的需求。

EHDE和WHO-SVM模型在齿轮箱故障诊断中的应用

针对现有齿轮箱故障诊断方法对数据长度敏感的缺陷,提出了一种基于增强层次多样性熵(EHDE)和野马算法(WHO)优化支持向量机(SVM)的齿轮箱故障诊断模型。首先,传统熵值特征提取方法在特征提取阶段对数据样本的长度比较敏感,为此提出了增强层次多样性熵,并将其作为特征提取指标用于提取齿轮箱的故障特征;其次,采用WHO算法对SVM模型的参数进行了优化,建立了参数最优的WHO-SVM分类器;最后,将故障特征样本输入至WHO-SVM分类器中进行了训练和识别,完成了样本的故障识别;利用齿轮箱数据集分别从数据长度敏感性、算法特征提取时间、模型诊断性能三种角度对EHDE、精细复合多尺度样本熵、精细复合多尺度模糊熵、精细复合多尺度排列熵、精细复合多尺度散布熵、精细复合多尺度波动散布熵进行了对比研究。研究结果表明:EHDE方法对数据长度的要求较低,在数据长度为512时即可以取得99.1%的平均识别准确率,在诊断稳定性和诊断精度方面均优于其他对比方法;在算法的泛化性实验中,EHDE方法能够以98%的准确率识别齿轮箱的不同故障类型,具有明显的泛化性和通用性。

动态价格约束下生鲜无人零售点选址-路径方法研究

“新零售”创新了生鲜无人零售的渠道和销售模式,通过研究网络化无人售货的技术优势,引入在线动态定价机制,解决了生鲜无人销售品控难、客户黏度低的问题,有效减低了系统综合成本;同时构建了一种面向生鲜商品的无人零售的干线和支线混杂配送模型,在多目标求解、问题解耦和PSO全局优化等方面进行突破,建立了一种动态价格约束下的带时间窗选址-路径二级运输模型(2E-dPLRPTW),并进行了案例验算,确认该方法能有效提升生鲜商品无人售卖的效益。

海洋垃圾回收船舶路径多目标优化

海洋垃圾污染不仅会破坏海洋生态系统,而且对人类健康也会造成一定的威胁。派遣船舶对近海漂浮的海洋垃圾回收是减少海洋垃圾污染的一种有效方法。由于风和洋流的影响,海洋垃圾的位置是不断变化的。因此,对基于可移动时间窗的海洋垃圾回收船舶路径优化进行了研究,即对同一个垃圾点,设置多个时间跨度适当的时间窗,船舶可以选择在其中一个时间窗内执行收集任务。为了降低船舶回收成本和碳排放量,建立了船舶走行时间和碳排放量的多目标优化模型。并将改进的自适应大规模领域搜索算法作为领域搜索机制,嵌入速度优化,提出了一种基于分解策略的多目标优化算法对其进行求解,即MOEA/D ALNS算法。并通过一组数值实验验证了所提模型和算法的有效性。实验结果表明,与基于遗传算法的MOEA/D算法和NSGA II算法相比,MOEA/D ALNS算法在运行时间和结果上均有明显的优势。此外,对时间窗数量分析可得,随着可移动时间窗数量增多,船舶回收平均成本和碳排放量降低。

自适应窗口旋转优化短时傅里叶变换的变转速滚动轴承故障诊断

针对短时傅里叶变换(STFT)中固定窗效应所导致的能量集中度不高的问题,提出了一种自适应窗口旋转优化短时傅里叶变换(AWROSTFT)的变转速滚动轴承故障诊断方法。通过变分模态分解(VMD)对原始振动信号进行降噪,并利用粒子群优化算法(PSO)解决了VMD参数选择困难的问题;利用切线思想对STFT中水平窗口自适应匹配一系列的旋转算子,使得窗口旋转方向接近甚至等于瞬时调频率,提高了时频表示的能量集中度;计算出谱峰检测法提取到的瞬时频率与转频的平均比值,将得到的结果与轴承的故障特征系数进行匹配,以此实现变转速工况下滚动轴承的故障诊断。仿真和实验的结果都表明,本文所提方法能够兼顾PSO-VMD和AWROST-FT的优势,通过切线思想自适应的旋转窗口使得信号与窗函数在全局上的夹角都为零,从而达到提高能量集中度和锐化时频脊线的目的,实现了变转速工况下滚动轴承的故障诊断。

考虑作业环节完整性的企业车站进路选择优化

传统企业车站进路选择优化问题,即当多项作业出现在同一时空,以行车作业优先,调车作业在冲突节点前停车等待至行车作业驶离冲突节点再继续作业。现实生产过程中易造成调车作业长时间停滞等待,导致调车作业无法准时到达目标地点,破坏了调车作业环节的完整性,损害了企业车站的利益。基于企业车站行车作业与调车作业并存且调车作业数量要远大于行车作业的特点,同时考虑调车作业环节完整性,将调车作业设定一个能充分完成作业的时间标准值,并动态实时更新作业过程中节点占用时间,以判断进路冲突并进行疏解,建立以调车作业时间成本、行车作业时间成本最小的多目标进路选择优化模型。为加快遗传算法的搜索效率,结合K短路算法特点改进了遗传算法,并且以进路为基本单元针对性地设计了个体编码方案、交叉变异策略,求解该模型。最后,以具有代表性的某企业车站为例进行了验证。研究结果表明:考虑作业环节完整性的进路选择优化模型得到的进路选择方案能够有效地避免作业冲突,并且能够依据作业的优先级为车站作业选择合理的进路,快速有效地得出满意的作业方案,满足车站工作的需求。与传统进路选择方案相比,总的作业时间降低了23.26%。研究成果对该企业车站进路选择具有一定的参考价值。

考虑物流特性用于削峰填谷的纯电重卡双层协调优化调度方法

针对纯电重卡大规模充电给电网带来的负荷峰值大、运行成本高等问题,提出了用于削峰填谷的双层协调优化调度方法。首先,考虑物流特性对纯电重卡进行负荷预测,并获取纯电重卡的行驶行为,得到充电负荷曲线,同时利用模糊C均值聚类将充电负荷分为可调度与不可调度两类,计算其可调度潜力;其次,以总负荷方差最小为目标函数,构造上层优化模型,制定纯电重卡日充电方案;然后,将上层寻优的日充电方案传递到下层,考虑柔性时间窗约束,以纯电重卡运行成本最低为目标函数,构造下层优化模型,实现考虑充电负荷方差与运行成本的双层优化调度;最后,在主从博弈框架下利用自适应粒子群算法求解双层协调优化调度模型,采用某物流区域实际纯电重卡数据进行仿真验证。结果表明,提出的双层优化调度方法能够在有效平滑负荷曲线的同时,降低纯电重卡的运行成本。

基于遗传算法的飞机垃圾车计划恢复模型

利用人工经验调度地面保障车辆,难以在突发状况发生时确保地面车辆的合理调度,从而导致航班的集体延误和地面保障车辆资源的浪费.针对机场垃圾车调度的上述问题,在硬时间窗限制及多种约束条件下,以飞机垃圾车调度总成本最小为目标函数构建计划恢复模型.算例分析结果表明:相对于人工恢复调度费,该文计划恢复模型调度费节约了46.44%.因此,该文计划恢复模型具有可行性.

混合多目标灰狼算法求解多目标VRPTW问题

针对带时间窗的多目标车辆路径规划问题,建立了最小化总成本和均衡度的多目标车辆路径优化模型,并提出了一种混合多目标灰狼算法进行求解。主要设计3点策略:(1)设计新的编码解码方式实现连续灰狼位置向量向离散客户序列的转化。(2)采用收敛性指标和分布性指标来进行决策个体的选择。(3)设计了多种删除、插入算子实施局部路径优化。为说明算法的有效性,以Solomon中的部分算例为例,将该算法与MOIGA和改进的ACO算法进行实验对比。实验结果表明,所提出的混合多目标灰狼算法能找到更好的Pareto解,并且性能优于其他进化算法。