Concise review of relaxations and approximation algorithms for nonidentical parallel-machine scheduling to minimize total weighted completion times

A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.

New HB-weighted time delay estimation algorithm under impulsive noise environment

The traditional HB-weighted time-delay estimation （TDE） method degenerates under the impulsive noise environment. Two new time-delay estimation methods are proposed based on fractional lower order statistics （FLOS） according to the impulsive characteristics of fractional lower order α-stable noises. Theoretic analysis and computer simulations indicate that the proposed covariation based HB weighted （COV-HB） algorithm can suppress impulsive noises in one received signal for 1 ≤α≤ 2, whereas the other proposed fractional lower order eovariancebased HB weighted （FLOC-HB） algorithm has robust performance under arbitrary impulsive noise conditions for the whole range of 0 〈α≤ 2.

Data point selection for weighted least square fitting of cavity decay time constant

For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.

Fabrication scheduling on a single machine to minimize the weighted sum of product completion time

In this paper, a fabrication scheduling problem concerning the production of components at a single manufacturing facility was studied, in which the manufactured components are subsequently assembled into a finite number of end products. Each product was assumed to comprise a common component to all jobs and a unique component to itself. Common operations were processed in batches and each batch required a setup time. A product is completed when both its two operations have been processed and are available. The optimality criterion considered was the minimization of weighted flow time. For this scheduling problem, the optimal schedules were described in a weignted shortest processing time first （WSPT） order and two algorithms were constructed corresponding to the batch availability and item availability, respectively.

ABSOLUTE CONTINUITIES OF EXIT MEASURES AND TOTAL WEIGHTED OCCUPATION TIME MEASURES FOR SUPER-α-STABLE PROCESSES

Suppose X is a super-α-stable process in R^d, （0 〈 α〈 2）, whose branching rate function is dr, and branching mechanism is of the form ψ（z） = z^1＋β （0 〈0 〈β ≤1）. Let Xγ and Yγ denote the exit measure and the total weighted occupation time measure of X in a bounded smooth domain D, respectively. The absolute continuities of Xγ and Yγ are discussed.

Weighted average consensus problem in networks of agents with diverse time-delays

This paper studies the weighted average consensus problem for networks of agents with fixed directed asymmetric unbalance information exchange topology. We suppose that the classical distributed consensus protocol is destroyed by diverse time-delays which include communication time-delay and self time-delay. Based on the generalized Nyquist stability criterion and the Gerschgorin disk theorem, some sufficient conditions for the consensus of multi-agent systems are obtained. And we give the expression of the weighted average consensus value for our consensus protocol. Finally, numerical examples are presented to illustrate the theoretical results.

Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting

Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.

Real-Time Update of Sequence Placement Logic for High Arch Dams Based on Evidence Weight Discount

Sequence placement logic plays a significant role in construction simulation of high arch dams and directly affects the simulation process and results. To establish a sequence logic for dam block placement, the construction scheme, real-time construction process, and random factors of the site all need to be considered in detail. There are few studies available currently that take all these factors into consideration. To address this problem, a real-time update of sequence placement logic for high arch dams based on evidence weight discount is proposed in this study. First, the subjective weight of the dam block sequence priority criteria is built using a consistent matrix method based on the construction scheme. Second, using evidence theory, dynamic objective weight of the priority criteria and basic probability assignment is built. Finally, using a weight self-adaptive adjustment method and comprehensive evidence discounting, the placing probabilities of different dam blocks are obtained. A case study indicates that this method can realize real-time update of sequence placement logic. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

基于ICEEMDAN和时变权重集成预测模型的变压器油中溶解气体含量预测

为了实现对变压器油中溶解气体体积分数的精确预测,同时克服仅使用单一预测模型导致预测精度及泛化能力不足的局限,提出了一种基于改进完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)和灰色关联系数时变权重集成预测模型的变压器油中溶解气体预测方法。首先将溶解气体含量序列模态分解为一系列具有不同时间尺度的子序列。然后,使用门控循环神经网络和麻雀搜索算法优化支持向量机对各子序列进行训练,组合为一个集成预测模型;并比较不同预测方法的预测精度,计算灰色关联系数时变权重,形成各子系列的预测结果。最后将各子序列的预测结果叠加重构,得到最终预测结果。算例分析结果显示:该方法单步预测的均方根误差、平均绝对误差和相关系数分别为0.593、0.422和0.768,相比其他算法在预测精度上有明显提升,同时具有很强的泛化性能,可以为油浸式变压器内部状态监测提供依据。

Assessment the Optimal Effect of Time of Repetition: Extrinsic Pulse Parameter on Gd-DTPA Enhanced, Spin-Echo T1-Weighted MR Images under Low Magnetic Field Strength

The contrast agent concentration, the time of repetition (TR) and magnetic field strength are significant parameters that influence for the accurate signal intensity (SI) in quantitative Magnetic Resonance Imaging (MRI). Therefore, this study was conducted to investigate and refine the dependence and the optimal effect of Time of Repetition (TR) on the relationship between signal intensity and Gd-DTPA (Gadolinium-diethylene-triaminepenta-acetic acid) concentration, after applying two-dimensional (2D) Spin Echo (SE) pulse sequence under low-field MRI. In addition to that, the optimal concentration of Gd-DTPA at given sequence parameters at low-field MRI was also evaluated. A water-filled phantom was constructed for a range of Gd-DTPA concentrations (0 - 6 mmol/L) and the mean signal intensities (SIs) were assessed in the defined region of interest on T1-weighted images with different TR values (40 - 2000 ms). The generated signal-concentration curves for Gd-DTPA revealed that increasing TR was associated with the increase of the overall SIs and the maximum relationship between SI to concentration. Moreover, the required Gd-DTPA concentration to produce the maximum SI was associated to decrease with the increase of TR. In addition to this, the application of beyond 100 ms TR values in this study with relatively higher concentrations (beyond 1 - 2 mmol/L) has resulted predominantly non-linear patterns in the signal-concentration curves and it appears the saturation or decay of the SIs due to T2 effect. From these results, it can be suggested that the selection of relatively lower Gd-DTPA concentration ( mmol/L) with less than 800 ms (<800 ms) TR values can produce a better linear relationship between the concertation and SIs in T1-weighted SE low field contrast-enhanced MRI. Furthermore, this study also outlined the significance and necessity of the optimization of TR in SE sequence in low field MRI prior to a particular examination.

分割式赛汝生产系统激励评价

突发事件及随机冲击影响了管理情境,对时序动态综合评价方法有效性提出了新要求。外部环境复杂多变使得一类时序动态综合评价值波动显著异于以往状态,如评价值出现骤跌然后缓慢爬升至较低水平并持续波动,此时被评价对象的评价值已经击穿经典双激励控制线的负激励线并持续处于下行状态,从而使得经典激励评价方法有效性有待改进。面对这种情况,提出具有情境调节特征的递归激励评价方法,针对分割式赛汝(SERU)生产系统的员工特征,首先通过对赛汝多能工的生产状态进行马氏过程预测,预测出多能工下一生产状态的概率分布,根据得到的概率分布得出每一阶段的递归值并且结合双激励线方法对多能工的递归值进行激励评价,最后结合犹豫模糊函数求解具有隶属度的时间权重,得到具有隶属度时间权重的评价值。通过考虑结合赛汝生产系统这一时代新型生产系统,证明该方法具有更好的柔性和鲁棒性。

Weighted Pseudo Almost Periodic Solutions for a Class of Hematopoiesis Model with Time-Varying Delay

In this paper, firstly, a notion of a class of generalized weighted pseudo al- most periodic function is introduced, then we investigate some basic and essential properties of the space that consists of these functions. Finally, we study the exis- tence of weighted pseudo almost periodic solutions to hematopoiesis model with time- varying delay.

基于时变权重模型预测的双向DC-DC优化控制方法

双向DC-DC变换器是电动汽车的关键电压变换部件,提高其动态负载下的响应速度与鲁棒性对电动汽车低压供电系统有重要意义。提出一种基于模糊时变权重的MPC(model predictive control)控制方法。基于状态空间平均法建立变换器动态模型。基于MPC原理搭建变换器MPC模型,实现对电压的跟踪控制;在分析权重对动态负载下电压跟踪性能的影响的基础上,设计权重模糊调节器,实现权重的在线调节。基于HM-cSPACE搭建实验平台验证所提方法的准确性。在Matlab/Simulink环境下与PI控制和传统MPC控制进行对比,结果表明:在负载变化时采用时变权重MPC控制的变换器有更好的动态性能和鲁棒性,超调量降低26.1%。该方法对提高双向DC-DC变换器动态负载下的优化控制具有重要意义。

在线实时质量智能分析仪的设计与实现

宠物食品研发环节中决定性的因素是宠物对食品的喜好程度,如果市场面向的是全球的宠物,其研发所面临的问题更为复杂。宠物饮食数据收集方式困难、全球数据汇总不便利和不及时、数据监测周期长等问题都制约着其食品研发的进程。文章设计一个全球宠物食品研究系统,由在线实时质量智能分析仪长期或定期收集宠物每次相同时间吃食品质量的变化,通过无线通信的方式,加密发送到指定的服务器,食品研发人员再根据全球汇总回来的数据进行筛选分析。以宠物对食品的偏爱程度,对产品成分进行调整优化。通过实验以及实际使用证明,该系统具有良好的实时食品研发数据收集功能和较好的数据分析功能。

太行山复杂地形下华北暖季极端降水的时空分布特征

基于2012—2021年5—9月华北五省的逐日降水资料和台站地形高度数据,统计分析了华北全区及各子区域极端降水事件的降水量及其强度和频次的时空分布特征;并运用地理加权回归(GWR)模型分析得到极端降水事件的降水量、强度及频次与海拔高度之间的关系。结果表明:1)华北区域极端降水量的时间变化均呈多波动特征且区域差异性显著,太行山以西高原和以东平原降水频次多、波动明显且强度较弱,太行山南段以南平原降水频次少、变化平缓而强度明显偏强。2)极端降水量的空间分布呈现南北少、中间多的型态分布,降水量大值区分别位于燕山东南侧和太行山南段晋冀豫三省交界处;极端降水高频站点主要聚集在晋东南地区;日最大降水量超过300 mm的站点主要集中在太行山脉和燕山山脉与华北平原的过渡地带。3)华北区域38°N以北,极端降水量、降水频次、强度和日最大降水量均随海拔高度的升高而减小;38°N以南,山西南部临运地区降水量随海拔高度的升高而显著增加。由于降水频次和强度与地形均存在正相关而导致,太行山附近降水量随海拔高度的升高而减小的贡献主要在于降水强度而非降水频次。

一次哺乳母乳摄入量与吸吮时间相关性及其他影响因素的研究

目的 研究一次哺乳母乳摄入量与吸吮时间的相关性,并探讨其他相关的影响因素。方法 2015年6月至7月在深圳市罗湖区妇幼保健院儿童保健门诊开展母乳喂养摄入量横断面调查,招募足够1~12月龄婴儿及其母亲,观察一次正常哺乳过程,并进行问卷调查。严格定义并记录哺乳时婴儿吸吮时间,使用称重法获得一次哺乳母乳摄入量,采用问卷调查母子健康信息和喂养情况。使用多元线性逐步回归法建立包括吸吮时间等因素影响一次哺乳母乳摄入量和摄入平均速率的模型,并进行统计分析。结果 经筛选共纳入150对研究对象进行分析。一次哺乳母乳摄入量与吸吮时间呈直线相关(r=0.57,P<0.05)。单因素分析结果显示,所有因素的组间吸吮时间差异均无统计学意义(P>0.05),右侧哺乳乳房的一次哺乳母乳摄入量明显高于左侧[(61.14±32.14)g vs.(49.88±29.35)g,t=2.24,P<0.05];右侧哺乳乳房的母乳摄入平均速率明显高于左侧[(8.12±4.48)g/min vs.(6.66±3.38)g/min,t=2.67,P<0.05],母亲为家庭主妇/休产假的母乳摄入平均速率明显高于有工作者[(8.02±4.22)g/min vs.(6.60±3.65)g/min,t=-2.17,P<0.05],5~12月龄婴儿的母乳摄入平均速率明显高于1~4月龄婴儿[(8.15±4.25)g/min vs.(6.67±3.70)g/min,t=-2.27,P<0.05]。多因素分析结果显示,在1~4月龄婴儿中,吸吮时间、婴儿体重和母亲右侧乳房哺乳与一次哺乳母乳摄入量均呈正相关(R值分别为0.61、0.22、0.36,P<0.05),婴儿体重和母亲右侧乳房哺乳与母乳摄入平均速率均呈正相关(R值分别为0.22、0.27,P<0.05);在5~12月龄婴儿中,吸吮时间与一次哺乳母乳摄入量呈正相关(R=0.61,P<0.05),婴儿体重与母乳摄入平均速率呈正相关(R=0.26,P<0.05)。婴儿性别、婴儿年龄、婴儿出生体重,以及24h母乳喂养频率、母亲体质量指数、产次、母亲年龄、母亲职业均对两个年龄组一次哺乳母乳摄入量和母乳摄入平均速率均无显著性影响(P>0.05)。结论 影响一次哺乳母乳摄入量的显著性因素为吸吮时间和婴儿体重,在小年龄段婴儿中还包括哺乳乳房侧。需要进一步研究影响母乳摄入量的其他因素,如母乳喂养类型和哺乳间隔等。

基于PCA-ShapeDTW-QWGRU的分布式光伏集群短期功率预测

针对分布式光伏短期功率预测建立基于主成分分析、改进的动态时间规整算法与量子加权门控循环单元(PCAShapeDTW-QWGRU)的集群功率预测模型。针对集群划分不够精细、光伏电站数据蕴含的信息难以捕捉的问题,提出基于主成分分析结合密度聚类算法(PCA-OPTICS)的集群划分方法;针对目前选取代表电站与集群相似性较低的问题,提出基于改进的动态时间规整算法(ShapeDTW)的代表电站的选取方法,利用ShapeDTW度量相似性距离,选取最小值作为代表电站,并利用基于均方根传播梯度下降法优化的量子加权门控循环单元(RMSprop-QWGRU)模型进行预测;为了解决代表电站与集群功率的变换系数转换差异较大的问题,采用实时变换系数对代表电站进行集群功率值预测计算。实验结果表明,所提方法能有效提升光伏集群功率预测的精度。

基于遥感时序物候特征的耕地非粮化多模式监测方法

耕地非粮化对粮食生产和农业可持续发展构成潜在威胁,精准监测不同的耕地非粮化类型对制定针对性的农业管理政策至关重要。该研究以河北省石家庄市藁城区为研究区,首先采用最大类间方差算法(OTSU)提取果园和耕地范围,然后利用Google Earth Engine(GEE)云计算平台构建了基于Sentinel-2遥感数据的特征集,包括光谱特征、物候特征和NDVI(normalized difference vegetation index)时序特征。结合面向对象分割和随机森林(radom forest, RF)、时间加权的动态时间规整(time-weighted dynamic time warping, TW-DTW)算法,构建了4种不同的分类模式用于提取粮食作物和露天蔬菜、大棚种植等非粮食作物。通过选择最优模式,提取了研究区2019-2022年间不同非粮化类型的空间分布信息,并探讨了不同模式的优点和局限性。结果表明:1)采用面向对象的机器学习模式进行耕地内作物分类的精度最佳,两个生长季内总体精度分别达到93.23%和90.10%,Kappa系数分别达到0.91和0.88;2)基于时间序列匹配的模式在区分粮食作物和其他地类方面表现出较高的准确性,冬小麦、玉米和大豆的用户精度分别高于95.60%、74.70%、82.70%,制图精度分别高于97.70%、86.40%、93.10%;3)利用面向对象的机器学习模式进行耕地非粮化信息提取,在两个作物生长季的总体精度为87.00%和81.00%。分析耕地非粮化结果发现,藁城区2019-2022年的年际性非粮化面积为2 753.09 hm^(2),其中果园占比最高;而季节性非粮化结果显示,秋粮非粮化面积(3 174.86 hm^(2))明显高于夏粮非粮化面积(1 060.27 hm^(2))。该研究利用Sentinel-2时序遥感数据,为一年两熟区耕地非粮化监测提供一种新的思路,可以为制定差异化农业管理政策提供依据。

A Simple Additive Weighting Method for Time-SeriesMultiindices Decision Making and Its Applications

This paper takes the evaluation of overall economic benefit by an example and proposes a simple additive weighting method for time-series multiindices decision making. The method can automatically determine the weight coefficients among the multiindices and the years respectively and it also can obtain the objective evaluation results and conclusions.