二维矩形Strip Packing问题的算法研究与改进

二维矩形Strip Packing问题的约束条件及目标函数与基本型二维矩形Packing问题类似,都是在有限的矩形容器中,有效地摆放各个矩形块,以最大化容器利用率为目标。为了解决这一NP-hard问题,该文在邓见凯、王磊提出的拟人型全局优化算法的基础上进行了深入的算法研究与改进。针对Strip Packing问题特点,提出了QHG(Quasi-Human Group)算法,其核心改进涵盖了多个方面,包括扩充初始点集合、删除和替换评价标准以及扩大邻域空间搜索范围。和单个局部极小值点的迭代相比,对局部极小值点集合进行迭代所生成布局优度更高,跳坑策略用于跳出局部极小值点,将搜索引向有希望的区域,优美度枚举有望进一步提高布局优度。通过这些措施,QHG算法更好地模拟人类决策过程,提高了全局搜索的效率。为评估QHG算法性能,对8组标准问题实例(C组、N组、NT组、CX组、NP组、ZDF组、2sp组、bwmv组)进行了大量实验。实验结果表明,QHG算法生成的布局优度优于当前国际文献中的几种较先进算法,展现了其在Strip Packing问题上的卓越性能。

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Molecular packing tuning via chlorinated end group enables efficient binary organic solar cells over 18.5%

Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Effect of the Particle Packing Configuration on Fixed Bed Performance

Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges related to inadequate mass transfer and a high pressure drop caused by the non-uniform void fraction distribution.To enhance the overall performance of fixed beds,the impact of different packing configurations on performance was investigated.Experimental and simulation methods were used to investigate the fluid flow and mass transfer performances of various packed beds under different flow rates.It was found that structured beds exhibited a significantly lower pressure drop per unit length than conventional packed beds.Furthermore,the packing configurations had a critical role in improving the overall performance of fixed beds.Specifically,structured packed beds,particularly the H-2 packing configuration,effectively reduced the pressure drop per unit length and improved the mass transfer efficiency.The H-2 packing configuration consisted of two parallel strips of particles in each layer,with strips arranged perpendicularly between adjacent layers,and the spacing between the strips varied from layer to layer.

Time-resolved characteristics of a nanosecond pulsed multi-hollow needle plate packed bed dielectric barrier discharge

In this paper,self-designed multi-hollow needle electrodes are used as a high-voltage electrode in a packed bed dielectric barrier discharge reactor to facilitate fast gas flow through the active discharge area and achieve large-volume stable discharge.The dynamic characteristics of the plasma,the generated active species,and the energy transfer mechanisms in both positive discharge(PD)and negative discharge(ND)are investigated by using fast-exposure intensified charge coupled device(ICCD)images and time-resolved optical emission spectra.The experimental results show that the discharge intensity,number of discharge channels,and discharge volume are obviously enhanced when the multi-needle electrode is replaced by a multihollow needle electrode.During a single voltage pulse period,PD mainly develops in a streamer mode,which results in a stronger discharge current,luminous intensity,and E/N compared with the diffuse mode observed in ND.In PD,as the gap between dielectric beads changes from 0 to250μm,the discharge between the dielectric bead gap changes from a partial discharge to a standing filamentary micro-discharge,which allows the plasma to leave the local area and is conducive to the propagation of surface streamers.In ND,the discharge only appears as a diffusionlike mode between the gap of dielectric beads,regardless of whether there is a discharge gap.Moreover,the generation of excited states N_(2)^(+)(B^(2)∑_(u)^(+))and N2(C^(3)Π_(u))is mainly observed in PD,which is attributed to the higher E/N in PD than that in ND.However,the generation of the OH(A^(2)∑^(+))radical in ND is higher than in PD.It is not directly dominated by E/N,but mainly by the resonant energy transfer process between metastable N_(2)(A^(3)∑_(u)^(+))and OH(X^(2)Π).Furthermore,both PD and ND demonstrate obvious energy relaxation processes of electron-to-vibration and vibration-to-vibration,and no vibration-to-rotation energy relaxation process is observed.

Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

Simulation and Optimization of Energy Efficiency and Total Enthalpy Analysis of Sand Based Packed Bed Solar Thermal Energy Storage

This study is focused on the simulation and optimization of packed-bed solar thermal energy storage by using sand as a storage material and hot-water is used as a heat transfer fluid and storage as well.The analysis has been done by using the COMSOL multi-physics software and used to compute an optimization charging time of the storage.Parameters that control this optimization are storage height,storage diameter,heat transfer fluid flow rate,and sand bed particle size.The result of COMSOL multi-physics optimized thermal storage has been validated with Taguchi method.Accordingly,the optimized parameters of storage are:storage height of 1.4m,storage diameter of 0.4 m,flow rate of 0.02 kg/s,and sand particle size 12 mm.Among these parameters,the storage diameter result is the highest influenced optimized parameter of the thermal storage fromthe ANOVA analysis.For nominal packed bed thermal storage,the charging time needed to attain about 520 K temperature is more than 3500 s,while it needs only about 2000 s for the optimized storage which is very significant difference.Average charging energy efficiency of the optimized is greater than the nominal and previous concrete-based storage by 13.7%,and 13.1%,respectively in the charging time of 2700 s.

DS Smith北美公司推出新的PackRight 2.0多元协作体验系统

7月26日,DSSmith推出了一种多元协作体验系统——PackRight 2.0。新产品的包装设计以互动研讨会为特色,帮助快消品(FMCG)、电子商务和其他行业的企业创新,使用DS Smith专有的循环设计指标可提高包装的可持续性。

得世推出P5 350 HS Pack瓦楞新产品

作为数字印刷和生产技术制造商,得世通过P 5350 HS Pack的推出扩展了其产品组合,P5 Pack系列计划于2024年第三季度上市。P5 Pack系列是专为瓦楞展示架和包装生产量身定制的产品,P5350 HS Pack将P5混合印刷系统的多功能性与在POP/POS和零售市场使用的各种材料上进行印刷的能力相结合。

基于动作空间求解二维矩形Packing问题的高效算法

对于二维矩形Packing这一典型的NP难度问题,在黄文奇等人提出的拟人型穴度算法的基础上,通过定义动作空间来简化对不同放入动作的评价,使穴度的计算时间明显缩短,从而使算法能够快速地得到空间利用率较高的布局图案.实验测试了Hopper和Turton提出的21个著名的二维矩形Packing问题的实例.改进的算法对其中的每一个实例都得到了空间利用率为100%的最优布局,且在普通PC机上的平均计算时间未超过7分钟.实验结果表明,基于动作空间对拟人型穴度算法所进行的改进是明显而有效的.

求解方格packing问题的启发式算法

沿着拟物与拟人的途径,本文为一类具有NP难度的方格packing问题得到了实用的近似求解算法,以此算法为基础可以发展出一种为大规模集成电路芯片裁切工作做计算机辅助设计的高效的软件系统。

求解矩形Packing问题的砌墙式启发式算法

为求解正交矩形Packing问题提出了一个新颖而有效的砌墙式启发式算法.该算法主要基于砌墙式启发式策略,其思想主要来源于砖匠在砌墙过程中所积累的经验:基于基准砖的砌墙规则.对国际上公认的大量的Bench-mark问题例的计算结果表明,该算法的计算速度不仅比著名的现代启发式算法快,而且获得更优的高度.

基于粗精调技术的求解带平衡约束圆形Packing问题的拟物算法

带平衡约束的圆形Packing问题是以卫星舱布局为背景的具有NP难度的布局优化问题.文中建立了此问题相应的数学模型,同时提出了两个新的物理模型,并受工艺加工过程中"粗精加工"现象的启发,提出了基于粗精调技术的拟物算法QPCFA.该算法既兼顾了搜索空间的多样性以利于全局搜索,又能对有前途的局部区域进行精细搜索以找到相应的局部最优解.同时,在计算过程中引入禁忌技术和跳坑策略,以提高算法的求解质量.对国际上11个代表性的算例进行了计算,QPCFA更新了其中7个算例的最好记录,其余4个与目前的最好记录基本持平,且与目前的最好结果相比在计算精度上均有较大的提高.

基于加权分治技术的set packing精确算法

加权分治技术是算法分析中的一种新技术,该技术基于选择不同的量来描述分支子问题的大小,以求得到在最糟糕情况下最好的时间复杂度.setpacking问题是一典型的NP-hard问题,广泛应用于调度、代码优化和生物信息学等领域.本文对有n个子集的setpacking问题,引入符号全集变量N设计基于分支搜索策略的递归算法,并应用加权分治技术对算法加以分析,得到时间复杂度为O*(1.1686n+N)的精确算法,当N≤n/4时,比现有最佳的算法O*(1.2209n)更加有效.

动作空间带平衡约束圆形Packing问题的拟物求解算法

对于一个以卫星舱内设备布局为背景的具有NP难度的全局优化问题——带平衡约束的圆形Packing问题,提出了基于动作空间的拟物求解算法.在拟物下降遇到局部极小点的陷阱时,如何找到当前格局下的最空闲空间以使搜索过程跳到更有前景的区域去是设计跳坑策略的一个关键难点.借鉴求解矩形Packing问题中动作空间的概念,通过化"圆"为"方",将不规则的空闲空间近似为一系列规则的矩形空间,从而有效地解决了此难点.另外,将拟物法与提前中止、粗精调和自适应步长这3个拟人辅助策略相结合,以提高势能下降的效率.对3组共13个代表性算例的计算结果及与国内外代表性算法的比较表明,所提格局的外包络圆半径多为最小或次小,且在部分算例上找到了有更小外包络圆半径的格局,总体计算结果较好,且静不平衡量的精度较高.

求解平衡约束圆形Packing问题的快速启发式并行蚁群算法

带平衡约束圆形Packing问题属于NP-hard问题,求解困难.提出一种求解该问题的快速启发式并行蚁群算法.首先提出一种启发式方法:在轮盘赌选择定序的概率公式中增加质量因子和外围逆时针排列定位待布圆,并用它构造出多样性种群个体(相交圆数不超过3的布局方案).然后将蚁群优化与并行搜索相结合,使种群个体快速收敛到最优解或迭代出存在少量干涉的近似最优解(1～3个相交圆).若为后者,则基于物理模型用最速下降法将其快速调整成最优解.所采用的启发式方法、并行蚁群搜索机制和快速调整策略有机结合提高了算法的搜索精度和效率.数值实验表明该算法在性能指标上优于已存在的算法.

基于欧氏距离的矩形Packing问题的确定性启发式求解算法

使用拟人的策略,提出了基于欧氏距离的占角最大穴度优先的放置方法,为矩形Packing问题的快速求解提供了一种高效的启发式算法.算法的高效性通过应用于标准电路MCNC和GSRC得到了验证.

求解矩形packing问题的贪心算法

在货物装载、木材下料、超大规模集成电路设计等工作中提出了矩形packing问题。对这一问题,国内外学者提出了诸如模拟退火算法、遗传算法及其它一些启发式算法等求解算法。该文利用人类的智慧及历史上形成的经验,提出了一种求解矩形packing问题的贪心算法。并对21个公开测试实例进行了实算测试,所得结果的平均面积未利用率为0.28%,平均计算时间为17.86s,并且还得到了其中8个实例的最优解。测试结果表明,该算法对求解矩形packing问题相当有效。