Construction of LDPC Codes for the Layered Decoding Algorithm

The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check (LDPC) decoders, due to its high convergence speed. However, the pipeline operation of the layered decoder may introduce memory access conflicts, which heavily deteriorates the decoder throughput. To essentially deal with the issue of memory access conflicts, we propose a construction algorithm of LDPC codes, to which a constraint condition is added in the Progressive Edge-Growth (PEG) algorithm. The constraint condition can guarantee that for our constructed LDPC codes, the sets of all the variable nodes connected to the consecutive layers do not share any common variable node, which can avoid the memory access conflicts. Simulation results show that the performance of our constructed LDPC codes is close to the several other LDPC codes adopted in wireless standards. Moreover, compared with the decoder for IEEE 802. 16e LDPC codes, the throughput of our LDPC decoder has large improvement, while the chip resource consumption is unchanged. Thus, our constructed LDPC codes can be adopted in the high-speed transmission.

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

An efficient parallel algorithm for shortest pathsin planar layered digraphs

This paper presents an efficient parallel algorithm for the shortest path problem in planar layered digraphs that runs in O(log^3n) time with n processors. The algorithms uses a divide and conquer approach and is based on the novel idea of a one-way separator, which has the property that any directed path can be crossed only once.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

油藏不同开发阶段层面建模算法及实例

由于油藏不同开发阶段特征及其研究目的不同,三维地质建模中层面建模采取的模拟方法和模型精度均有所区别。从三维地质建模角度出发,将油藏开发划分为油藏评价、新区建产和老区调整3个阶段,通过港东二区五断块层面模拟过程中各种模拟算法分析及筛选,提出了层面模拟时,油藏评价阶段建模采用Kriging算法,网格精度为100 m×100 m×5.0 m;新区建产阶段建模采用Kriging算法或Global B-spline算法,网格精度为50 m×50 m×1.5 m;老区调整阶段建模采用Local B-spline算法或Converging average算法,网格精度为10 m×10 m×0.5 m,使层面模拟结果更符合地质情况及研究需要。

考虑水土互馈关系的区域水土资源联合优化配置

针对水土资源空间不匹配、水资源总量不足及土地资源利用程度不高等问题,以二元水循环理论为基础,构建考虑水土互馈关系的区域水土资源联合优化配置模型。该模型包括产水模块、水土联合配置模块和土地模拟模块,以GDP最大和基于生态绿当量的区域植被覆盖率(EGE-RVC)最高为目标函数,并提出了一种耦合约束法、逐次逼近法和非线性规划的三层嵌套算法进行求解。以河南省洛阳市为例对模型和方法进行验证,结果表明,洛阳市可利用水量增加了4463万m^(3),GDP提高了12.5%,EGE-RVC增加了2.6%,各用地类型缺水率不同程度下降。研究成果能够为区域水土资源优化配置研究提供新的思路和技术参考。

可重构低熵智能车间布局建模与优化

针对智能车间布局,采用单元布局的方式分别对单元间布局和单元内布局进行建模,设计涵盖物料搬运成本、时间损失成本、单元重构成本、设备成本、车间面积利用率和布局柔性等面向低熵化车间布局的多目标函数,给出包括边界约束、间距约束和固定位置约束等符合车间实际情况的定量约束条件。针对车间布局模型多变量、多约束和非线性的特点,设计一种基于精英策略和自适应交叉、变异算子的双层遗传元胞算法进行求解。通过典型企业作业车间布局改造实例分析,验证模型和算法的可行性和有效性。

AN IMPLICIT ALGORITHM OF THIN LAYER EQUATIONS IN VISCOUS，TRANSONIC，TWO－PHASE NOZZLE FLOW

Omitting viscosity along flow direction, we have simplified the dimensionless N-Sequations in arbitrary curved coordinate system as the thin layer equations. Using theimplicit approximate-factorization algorithm to solve the gas-phase governing equ-ations and the characteristic method to follow the tracks of particles, we then obtainedthe full coupled numerical method of two-phase.transonic, turbulent flow. Here, par- ticle size may be grouped, the subsonic boundary condition at entry of nozzle is ireatedby quasi-characteristic method in reference plane and the algebraic model is used forturbulent flow. These methods are applied in viscous two-phase flow. calculation of ro-cket nozzle and in the prediciton of thrust and specific impulse for solid propellant ro-cket motor. The calculation results are in good agreement with the measurerment va-lues. Moreover, the influences of different particle radius, different particle mass frac-tion and particle size grouped on flow field have been discussed, and the influences of particle two-dimensional radial velosity component and viscosity on specific impulse ofrocket motor have been analysed.The method of this paper possesses the advantage of saving computer time. More important, the effect is more obvious for the calculation of particle size being grouped.

一种航空装备实时系统任务快速调度方法

针对航空装备实时系统对多线程下实时任务快速调度的困难,提出了一种基于多层编码遗传算法并利用粒子群算法进行参数优化的实时系统任务快速调度方法。通过引入任务贡献矩阵,建立了以总消耗时间少和贡献值大的任务尽早完成为目标的染色体适应度值评价模型,避免重要任务的丢失。采用粒子群算法对多层编码遗传算法参数进行优化,避免陷入局部最优解,增加了收敛速度。最后对调度方法进行仿真验证,仿真结果表明,本文提出的实时系统任务快速调度模型与方法可以优化任务的执行时间,保障任务的顺利完成。通过对粒子群算法优化前和优化后的调度情况的对比,证明了优化后的模型的调度性能指标要优于参数优化前的模型,调度速度和调度效果明显提升。

基于双层优化VMD-LSTM的农村超短期电力负荷预测

稳定的供电是农村发展建设的有力保障,而电力负荷水平是建设效果的重要衡量标准,因此建立精确的负荷预测模型可以更准确直观显现电力负荷情况,为供电公司制定决策提供有力支撑。由于LSTM负荷预测模型在数据预测方面存在收敛性差、预测精度不高等问题,为提高模型的预测精度,提出一种基于双层优化VMD-LSTM的超短期电力负荷预测方法。首先提出麻雀算法优化变分模态分解(sparrow variational mode decomposition,SVMD),通过SVMD将原始数据转化为模态分量(intrinsic mode functions,IMF);其次采用改进樽海鞘群算法(association salp swarm algorithm,ASSSA)优化LSTM模型。通过引入4种策略增强标准樽海鞘算法优化能力;最后将各模态分量分别代入到新模型并进行叠加预测。选取辽宁省某市某乡村10kV变压器真实历史负荷数据,以均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、拟合度(R^(2))作为评价指标,并与其他基础预测模型进行对比,结果表明,改进后的算法在计算精度、稳定性方面均优于其他基础预测模型。

考虑同层后道工序的柔性设备网络综合调度算法

针对柔性设备网络综合调度算法,难以合理选择加工设备加工相关工序进而影响产品完工时间的问题,该文提出考虑同层后道工序的柔性设备网络综合调度算法(SP-FENIS)。首先,采用逆序层优先策略,将各工序分配至逆序层待调度工序集;然后,提出均值逆序紧后路径策略,确定了各逆序层待调度工序集中工序的调度顺序;最后,提出最早完工时间策略和设备空闲插入策略,当工序在柔性设备上最早完工时间相同时,考虑了在柔性设备上的加工时间和同层后道工序的加工设备,确定了目标工序的加工设备以及加工时间。实例表明,和已有算法相比,该算法能够缩短产品完工时间。

考虑实时订单更新的拼车调度双层规划模型

针对订单实时更新的实际情况,为仍有待完成订单的司机持续分配任务,在保证司机收益增加的同时,提升拼车平台的派单效率。在考虑拼车系统服务质量与运行成本的基础上,基于平台角度构建了以司机总收益最大化为目标的双层规划模型,并给出求解该模型的双层算法:底层模型对拼车路径进行规划,设计改进的遗传算法求解;上层模型决定订单分配的顺序,通过贪心算法调用底层模型,比较收益变化后得到最终的调度结果。通过具体算例对模型进行验证,结果表明模型能够较快求解出订单匹配结果及行驶路线,说明了模型的可行性及算法的有效性,且计算结果能够反映实际场景。对比实验结果表明,模型在满足提升司机收益的基础上,能有效减少延误时间及降低行驶距离,对于实时订单更新场景下拼车调度问题的相关研究具有积极的参考意义。

年层自动识别方法在青藏高原冰芯定年中的应用

冰芯是重建过去气候环境变化的良好载体,高时间分辨率冰芯信息的提取依赖于冰芯准确定年。本文结合人工定年与基于隐马尔科夫模型的年层自动识别算法,在青藏高原的作求普冰芯、达索普冰芯和阿尼玛卿冰芯上进行了测试、验证与分析。在约束参考层、同步其他冰芯记录及审核复盘时,该方法在一定程度上减少了人工逐层识别的时间,并能通过算法自动推断年层所在位置及提供误差范围。首先,由人工提供一套基础定年模板及约束点,随后基于算法学习模板,计算冰芯代理变量的准周期性的循环,找到目标段落并探究其年层存在的可能性,通过前向-后向算法寻找所有可能的年层点位,并通过最大似然法从中选出最优解。在年层自动识别算法辅助下的作求普、达索普冰芯上部δ^(18)O定年结果与已知年表的累计误差小于2 a(3%),具有较好的一致性。进一步将此方法应用于阿尼玛卿冰芯上部的定年,得出该算法与人工定年在e_(1)[2.07,4.47]、e_(2)[8.31,9.71]部分存在1~2 a的差异。再经污化层校正,冰芯56.48 m处的定年结果为74 a,误差范围±3 a。本文设计的方法结合了人工判定年层的优势与算法的标准化分层处理能力,降低了在年代校正过程中的人工参与,可为未来青藏高原地区高分辨率冰芯数年层方法定年的误差量化研究提供科学参考。

基于3-layer中心度的社交网络影响力最大化算法

社交网络影响最大化问题是指如何寻找网络中有限的初始节点,使得影响的传播范围最广。一些贪心算法可以得到较好的影响范围,但是因时间复杂度太高而不适用于大型社交网络。基于度中心性的启发式算法简单但准确度不高;基于介数中心性、接近中心性等全局指标的启发式算法可以较好地识别影响力最大的节点,但计算复杂度也过高。考虑网络节点深层次结构对影响扩散的作用并权衡计算复杂度与准确度,定义了3-layer局部中心度,以计算节点的潜在影响力值。基于线性阈值模型,启发选择一部分种子节点:每一次都选取潜在影响力最大的节点作为种子节点进行激活;运用贪心算法选取剩下的一部分种子节点:每一次都选取具有最大影响增量的节点作为种子节点进行激活。实验表明,该混合算法具有很好的激活范围以及非常低的时间复杂度。

基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化

针对分布于区域电网不同网络节点的多座储能电站参与电网调频功率调度问题,该文提出一种基于集合经验模态分解(EEMD)和多目标遗传算法(MOGA)的火-多储系统调频功率双层优化策略。该策略包含火-储调频功率优化层和多储能电站调频功率优化层:上层计及火-储调配资源各自优势及剩余调频能力,构建火-储调频功率优化分配模型,完成火-储调频功率的分配;下层引入关于调频成本和荷电状态(SOC)的自适应权重系数,以调频成本最低和SOC均衡为优化目标,完成调频功率在多储能电站之间的分配。仿真结果表明,所提策略可以提升区域电网调频效果并降低调频成本,均衡控制多个储能电站的调频成本和SOC,可以防止经济性较好的储能电站长期处于SOC越限边缘状态,提升储能电站参与调频的积极性和可持续性。

面向工业物联网的区块链高效拜占庭容错共识算法

鉴于工业物联网多样性终端存在作恶风险,为满足共识过程中对高效率和安全可容错的需求,提出了基于信誉积分与双层动态的实用拜占庭容错(CD-PBFT)高效共识算法。信誉积分模型确保良好节点参与共识,移除故障节点;双层架构实现交易验证和读写操作的并行;自适应主节点算法随机选取信誉值高节点作为主节点并确保其安全性。实验结果表明,CD-PBFT在保持安全性与活性的基础上,相较于PBFT,网络交易时延平均降低34.8%,吞吐量平均提高25.2%,实现了对效率与安全容错性的双重要求。

大数据辅助审判的潜在风险与防范路径

大数据辅助审判技术在提升审判质效、实现司法公正等方面发挥重要作用,但应用该技术带来的潜在风险同样不应被忽视。大数据辅助审判在技术赋能层面面临着数据基础不坚实、信息安全存在隐患和算法运用产生风险等问题,在技术应用实效层面又存在着技术僭越辅助地位、部分特殊场景的强行应用引发负面效果等风险。风险产生的缘由主要在于大数据系统开发行为缺乏统一指引和规范、审判人员应用大数据技术存在思维误区以及技术瓶颈难以突破三个方面。为此,我们应加强对数据开发共享的统一指引,提升司法数据信息安全保护力度,完善针对算法的法律规制,通过原则确立、权限与程序设置将技术应用限制在辅助地位,构建大数据辅助审判技术动态分层适用制度。

SSA-MLP模型在岩质边坡稳定性预测中的应用

岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Search Algorithm,SSA)改进多层感知器(Multi-Layer Perceptron,MLP)的神经网络模型,并用于边坡稳定性预测、指标敏感性分析及参数反演。其次,将收集的1085组岩质边坡的几何参数和H B准则参数等作为输入变量,极限平衡理论Bishop法求解的安全系数作为输出变量,对SSA MLP模型进行训练学习和性能评估。最后,将该模型运用于25个边坡实例,验证模型的有效性。结果显示,该模型收敛速度快、精度高,为边坡稳定性分析和参数量化提供了一种新思路。

小样本纱线质量预测的机器学习算法适用性分析

为了解决当前基于神经网络的纱线质量预测模型针对小样本预测精度偏低和预测精度不稳定的问题,建立了随机森林(RF)算法预测模型、多层感知机神经网络(MLP)算法预测模型和线性回归(LR)算法预测模型,就各算法模型在小样本情况下对不同数据特点的数据集的敏感性、不同数据维度的敏感性和不同训练样本数的敏感性进行了预测性能对比试验。用决定系数和均方根误差进行模型预测性能评估。试验结果表明:在小样本情况下,相比于MLP算法和LR算法,大多数情况下RF算法预测准确性更高、预测精度稳定性更好、对小训练样本量的适应性更好,具有较高的综合预测性能。