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A novel physics-informed framework for reconstruction of structural defects

Applied Mathematics and Mechanics(English Edition) 2022年第11期43卷 1717-1730页

作者： Qi li Fushun liU Bin WANG DZliU zhenghua QIAN State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China College of Engineering Ocean University of ChinaQingdao 266100Shandong ProvinceChina School of Engineering University of East AngliaNorwich NR47TJU.K.

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process.However,the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results.Besides,the scattering signals collected from industry sectors are often noised and nonstationary.To address these issues,a novel physics-informed framework(PIF)for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper.Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions,a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals.This learning model is then adopted to assess and characterize the defect profiles in structures,improve the accuracy of the analytical model,and eliminate the impact of the noise pollution in the process of inspection.To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising,several numerical examples are carried out.The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method,and provides a valuable insight into the development of artificial intelligence(AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

关键词： physics-informed deep-learning reconstruction of defects denoising

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A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful life

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 177-187页

作者： Qing Xu Min Wu Edwin Khoo zhenghua Chen Xiaoli li the Institute for Infocomm Research A*STARSingapore 138632Singapore IEEE

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://***/batteryrul/battery_rul_early_prediction.

关键词： Deep learning early prediction lithium-ion battery remaining useful life(RUL)

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代谢工程改造酿酒酵母合成β-胡萝卜素

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食品与发酵工业 2024年第3期50卷 1-8页

作者：周颂李由然张梁丁重阳顾正华石贵阳徐沙江南大学粮食发酵与食品生物制造国家工程研究中心江苏无锡214122 江南大学生物工程学院江苏无锡214122

β-胡萝卜素广泛用于食品、饲料、医药和化妆品等领域,利用微生物细胞合成高附加值的天然β-胡萝卜素,具有重要的研究意义。如何调控代谢途径中相关基因的表达强度,是在酿酒酵母体内合成β-胡萝卜素的关键因素。该文利用来源于三孢布拉... 详细信息

β-胡萝卜素广泛用于食品、饲料、医药和化妆品等领域,利用微生物细胞合成高附加值的天然β-胡萝卜素,具有重要的研究意义。如何调控代谢途径中相关基因的表达强度,是在酿酒酵母体内合成β-胡萝卜素的关键因素。该文利用来源于三孢布拉氏霉Blakeslea trispora的β-胡萝卜素合成酶转化出发菌株酿酒酵母LFD18,酿酒酵母能够从头合成β-胡萝卜素,并在细胞中积累。在研究过程中,平板上发现一株菌落颜色明显较周围菌落更深地突变株命名为ZS19 reddish,经实验验证突变株的β-胡萝卜素产量显著提高。针对上述两种不同颜色菌落,进行转录组分析,主要是发现一些与产物合成有关的基因,通过GO与PATHWAY分析发现与碳代谢和脂质体合成有关的6个上调基因,hxk1、dpp1、lpp1、fdh1、hmg2和gre2表达水平上调。通过定量PCR和单个基因过表达验证,最终通过同时过量表达hxk1、dpp1、lpp1和fdh1基因构建命名为ZS20菌株,β-胡萝卜素摇瓶发酵产量提高到194.3 mg/L,是ZS19 reddish菌株的1.3倍,是出发菌株ZS19的2.1倍,最终ZS20菌株经发酵罐发酵后β-胡萝卜素产量为314.3 mg/L,因此,通过进一步PATHWAY分析构建重组菌或许可以提高β-胡萝卜素的产量。

关键词： β-胡萝卜素酿酒酵母转录组 qPCR 过表达 PATHWAY分析

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NiW/SAPO-11催化剂对煤焦油加氢尾油模型化合物二十烷加氢异构性能的影响

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燃料化学学报（中英文） 2024年第3期52卷 395-404页

作者：赵旭红吴玉起代正华钟梅靳立军刘洋亚力昆江·吐尔逊李建周玉生新疆大学省部共建碳基能源资源化学与利用国家重点实验室化工学院新疆煤炭清洁转化与化工过程重点实验室新疆乌鲁木齐830017 大连理工大学化工学院精细化工国家重点实验室煤化工研究所辽宁大连116024 新疆宣力环保能源股份有限公司新疆哈密839009

以SAPO-11分子筛为载体,采用机械化学法制备Ni系催化剂,引入W调节其电子结构、晶粒尺寸与形态分布以及催化剂的孔结构、酸性与酸量等,基于对XRD、TEM、BET、NH3-TPD、吡啶吸附红外等结果的深入分析,探究催化剂NiW配比对催化剂性质和煤... 详细信息

以SAPO-11分子筛为载体,采用机械化学法制备Ni系催化剂,引入W调节其电子结构、晶粒尺寸与形态分布以及催化剂的孔结构、酸性与酸量等,基于对XRD、TEM、BET、NH3-TPD、吡啶吸附红外等结果的深入分析,探究催化剂NiW配比对催化剂性质和煤焦油加氢尾油模型化合物正二十烷(n-C_(20))加氢异构性能的影响规律。结果表明,Ni/SAPO-11负载W后,比表面积不同程度增加,W质量分数为0.5%时比表面积达到最大值149 m^(2)/g;Ni的平均粒径减小,W质量分数为1%时降至最小值4.43 nm,比Ni/SAPO-11减小36%,且Ni^(0)的含量和表面酸量均最高。此外,W促进了Ni的还原,使得还原峰温向低温方向移动。XPS分析表明,随着W含量的增加,Ni^(0)的结合能降低,W5+的结合能升高。二十烷(n-C_(20))的加氢异构产物分布显示,3Ni1W/SAPO-11作用下n-C_(20)的转化率和异二十烷(i-C_(20))的收率均最高,分别为88.23%和75.72%,且以单支链异二十烷(Mono-i-C_(20))为主,收率达71.65%。在线取样结果显示,n-C_(20)在金属位点与酸功能的双重作用下先生成单支链异构体,随着反应的进行向多支链异构体转化,不稳定的多支链异构体会进一步裂解成小分子烷烃。

关键词： SAPO-11 长链烷烃 NiW催化剂加氢异构

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COPD急性加重老年患者拔管后行主动循环呼吸联合振动正压呼气训练效果观察

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护理学杂志 2024年第2期39卷 6-9页

作者：文丹梁拯华杨秀茹万丽何海燕绵阳市中心医院重症医学科四川绵阳621000

目的探讨慢性阻塞性肺疾病急性加重(Acute Exacerbation of Chronic Obstructive Pulmonary Disease,AECOPD)老年患者拔管后应用主动循环呼吸技术联合振动正压呼气技术行呼吸功能训练的效果。方法将收治于重症医学科及呼吸内科监护室的7... 详细信息

目的探讨慢性阻塞性肺疾病急性加重(Acute Exacerbation of Chronic Obstructive Pulmonary Disease,AECOPD)老年患者拔管后应用主动循环呼吸技术联合振动正压呼气技术行呼吸功能训练的效果。方法将收治于重症医学科及呼吸内科监护室的72例AECOPD老年患者随机分为对照组(35例)与试验组(37例),对照组气管插管拔管后采用主动循环呼吸技术进行呼吸功能训练,试验组在主动循环呼吸技术基础上联合振动正压呼气技术进行呼吸功能训练,比较两组拔管后48 h内再次气管插管事件、咳嗽咳痰及呼吸困难症状改善情况、肺功能及血气指标。结果两组48 h内再次气管插管率比较差异无统计学意义(P>0.05)。干预后试验组咳嗽咳痰评分、呼吸困难评分显著低于对照组,PaO 2显著高于对照组,而PaCO 2显著低于对照组(均P<0.05);试验组拔管后ICU停留时间显著短于对照组(P<0.05)。结论对AECOPD老年患者增加振动正压呼气技术辅助呼吸功能训练,可以有效促进呼吸道分泌物排出,改善临床症状,缩短ICU住院时间。

关键词：老年人慢性阻塞性肺疾病正压呼气气管插管主动循环呼吸技术振动正压呼气技术呼吸功能训练

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Effects of an attached functionally graded layer on the electromechanical behaviors of piezoelectric semiconductor fibers

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Applied Mathematics and Mechanics(English Edition) 2022年第9期43卷 1367-1380页

作者： Kai FANG Nian li Peng li zhenghua QIAN V.KOLESOV I.KUZNETSOVA State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen 518057Guangdong ProvinceChina Kotel’nikov Institute of Radio Engineering and Electronics of Russian Academy of Sciences Moscow 125009Russia

In this paper,we propose a specific two-layer model consisting of a functionally graded(FG)layer and a piezoelectric semiconductor(PS)layer.Based on the macroscopic theory of PS materials,the effects brought about by the attached FG layer on the piezotronic behaviors of homogeneous n-type PS fibers and PN junctions are investigated.The semi-analytical solutions of the electromechanical fields are obtained by expanding the displacement and carrier concentration variation into power series.Results show that the antisymmetry of the potential and electron concentration distributions in homogeneous n-type PS fibers is destroyed due to the material inhomogeneity of the attached FG layer.In addition,by creating jump discontinuities in the material properties of the FG layer,potential barriers/wells can be produced in the middle of the fiber.Similarly,the potential barrier configuration near the interface of a homogeneous PS PN junction can also be manipulated in this way,which offers a new choice for the design of PN junction based devices.

关键词： piezoelectric semiconductor(PS) functionally graded(FG)material composite structure PN junction

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2021年5月21日漾濞M_(S)6.4地震极震区地形效应对地震动特性的影响

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地震学报 2024年第1期46卷 144-156页

作者：张立宝傅磊周正华林国良陈苏刘爱文李小军中国地震局地球物理研究所北京100081 南京工业大学交通运输工程学院南京211816 云南省地震局昆明650216 北京工业大学城市建设学部北京100124

基于对2021年5月21日漾濞M_(S)6.4地震微观震中秀岭村进行的震害调查,利用流动观测地形台阵观测资料,分析了地形效应对地震动特性的影响。结果表明,若地震波垂直山脉走向入射,陡坡会放大地震波的高频部分,且不同高程处,山脉垂向低频差... 详细信息

基于对2021年5月21日漾濞M_(S)6.4地震微观震中秀岭村进行的震害调查,利用流动观测地形台阵观测资料,分析了地形效应对地震动特性的影响。结果表明,若地震波垂直山脉走向入射,陡坡会放大地震波的高频部分,且不同高程处,山脉垂向低频差异较大,山脉走向低频差异较小。利用谱衰减法计算高频衰减参数的场地影响项κ_(0),分析了水平和竖向分量的κ_(0)与场地软弱程度和卓越频率的相关性,其结果表明,在小尺度范围内,水平分量和竖向分量的κ_(0)均具有较强的空间不均匀性。

关键词：震害现象高频衰减参数场地效应地形效应

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The effect of NiO-Ni_(3)N interfaces in in-situ formed heterostructure ultrafine nanoparticles on enhanced polysulfide regulation in lithium-sulfur batteries

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Journal of Energy Chemistry 2022年第5期31卷 762-770页

作者： Jun Pu zhenghua Wang Pan Xue Kaiping Zhu Jiachen li Yagang Yao Key Laboratory of Functional Molecular Solids Ministry of EducationAnhui Provincial Engineering Laboratory for New-Energy Vehicle Battery Energy-Storage MaterialsCollege of Chemistry and Materials ScienceAnhui Normal UniversityWuhu 241002AnhuiChina National Laboratory of Solid State Microstructures College of Engineering and Applied SciencesJiangsu Key Laboratory of Artificial Functional MaterialsCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093JiangsuChina School of Chemical Engineering Northwest UniversityXi’an 710069ShaanxiChina Division of Nanomaterials and Jiangxi Key Lab of Carbonene Materials Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesNanchang 330200JiangxiChina

Inhibiting the “shuttle effect” of soluble polysulfides and improving reaction kinetics are the key factors necessary for further exploration of high-performance li-S batteries. Herein, an effective interface engineering strategy is reported, wherein nitriding of an Ni-based precursor is controlled to enhance li-S cell regulation. The resulting in-situ formed NiO-Ni_(3)N heterostructure interface not only has a stronger polysulfide adsorption effect than that of monomeric NiO or Ni_(3)N but also has a faster li ion diffusion ability than a simple physical mixture. More importantly, this approach couples the respective advantages of NiO and Ni_(3)N to reduce polarization and facilitate electron transfer during polysulfide reactions and synergistically catalyze polysulfide conversion. In addition, ultrafine nanoparticles are thought to effectively improve the use of additive materials. In summary, li-S batteries based on this NiO-Ni_(3)N heterostructure have the features of long cycle stability, rapid charging-discharging, and good performance under high sulfur loading.

关键词： NiO-Ni_(3)N heterostructure Interface effect Ultrafine nanoparticles li-S batteries Polysulfides

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不同CO_(2)浓度和氮肥水平对稻田呼吸速率的影响

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农业资源与环境学报 2024年第3期41卷 567-575页

作者：柯浩楠曹琰梅商东耀武熳秋帅斯樑李琪胡正华南京信息工程大学应用气象学院南京210044 河南省气象科学研究所中国气象局农业气象保障与应用技术重点实验室郑州450003

为研究不同CO_(2)浓度和氮肥水平对稻田呼吸速率的影响,利用12个开顶式气室开展田间试验,CO_(2)浓度设置对照(CK,自由大气CO_(2)浓度)、CO_(2)浓度比CK增加120μmol·mol-1(C1)和200μmol·mol-1(C2)3个水平,氮肥设置低氮(N1,15 g·m^(-... 详细信息

为研究不同CO_(2)浓度和氮肥水平对稻田呼吸速率的影响,利用12个开顶式气室开展田间试验,CO_(2)浓度设置对照(CK,自由大气CO_(2)浓度)、CO_(2)浓度比CK增加120μmol·mol-1(C1)和200μmol·mol-1(C2)3个水平,氮肥设置低氮(N1,15 g·m^(-2))和常规氮肥(N2,25 g·m^(-2))2个水平,试验共6种处理。结果表明:在相同施氮水平下,在灌浆期和蜡熟期,C1N1比CKN1处理的呼吸速率分别下降了23.4%(P=0.045)和49.1%(P=0.010);在抽穗期和灌浆期,C2N2比CKN2处理的呼吸速率分别升高了12.3%(P=0.009)和16.8%(P=0.047)。同一CO_(2)浓度下,不同施氮水平处理的呼吸速率表现为N2>N1,且在拔节期和抽穗期达到显著水平,在灌浆期达到极显著水平;其中在灌浆期和蜡熟期,C1N1比C1N2处理的呼吸速率分别下降了31.1%(P=0.004)和42.7%(P=0.010)。研究表明,大气CO_(2)浓度升高对稻田呼吸速率的影响因生育时期而异,氮肥减施可以降低稻田呼吸速率及CO_(2)累积释放量。

关键词： CO_(2)浓度氮肥呼吸速率稻田静态暗箱气相色谱

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Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful life Prediction

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1427-1439页

作者： Ruibing Jin Min Wu Keyu Wu Kaizhou Gao zhenghua Chen Xiaoli li Institute for Infocomm Research Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore School of Computer Science Liaocheng UniversityLiaocheng 252000 Macao Institute of System Engineering Macao University of Science and TechnologyTaipaMacao 999078China

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

关键词： Convolutional neural network(CNN) deep learning position encoding remaining useful life prediction

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