Combination of flexible multifunctional stealth technology properties such as electromagnetic(EM)and infrared(IR)stealth is crucial to the development of aerospace,military,and electronic fields,but the synthesis tech...Combination of flexible multifunctional stealth technology properties such as electromagnetic(EM)and infrared(IR)stealth is crucial to the development of aerospace,military,and electronic fields,but the synthesis technology still has a significant challenge.Herein,we have successfully designed and synthesized highly flexible MXene@cellulose lamellae/borate ion(MXCB)sheets with strong high‐temperature EM‐IR bi‐stealth through sequential bridging of hydrogen and covalent bonds.The resultant MXCB sheets display high conductivity and good mechanical features such as flexibility,stretchability,fatigue resistance,and ultrasonic damage.MXCB sheets have a high tensile strength of 795 MPa.Furthermore,MXCB sheets with different thicknesses indicate exceptional high‐temperature thermal‐camouflage characteristics.This reduces the radiation temperature of the target object(>300°C)to 100°C.The conductivity of MXCB sheet with 3μm thickness is 6108 S/cm and the EM interference(EMI)shielding value is 39.74 dB.The normalized surface‐specific EMI SE absolute shielding effectiveness(SSE/t)is as high as 39312.78 dB·cm2/g,which remained 99.39%even after 10,000 times repeated folding.These multifunctional ultrathin MXCB sheets can be arranged by vacuum‐assisted induction to develop EM‐IR bi‐stealth sheet.展开更多
针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进IN...针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。展开更多
基金supported by the Nanning Innovation and Entrepreneurship Leading Talents“Yongjiang Plan”Project of Guangxi Province,China(No.2021016)Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars of China(No.LR19C160001)the Scientific Research Foundation of Zhejiang A&F University(No.2019FR009).
文摘Combination of flexible multifunctional stealth technology properties such as electromagnetic(EM)and infrared(IR)stealth is crucial to the development of aerospace,military,and electronic fields,but the synthesis technology still has a significant challenge.Herein,we have successfully designed and synthesized highly flexible MXene@cellulose lamellae/borate ion(MXCB)sheets with strong high‐temperature EM‐IR bi‐stealth through sequential bridging of hydrogen and covalent bonds.The resultant MXCB sheets display high conductivity and good mechanical features such as flexibility,stretchability,fatigue resistance,and ultrasonic damage.MXCB sheets have a high tensile strength of 795 MPa.Furthermore,MXCB sheets with different thicknesses indicate exceptional high‐temperature thermal‐camouflage characteristics.This reduces the radiation temperature of the target object(>300°C)to 100°C.The conductivity of MXCB sheet with 3μm thickness is 6108 S/cm and the EM interference(EMI)shielding value is 39.74 dB.The normalized surface‐specific EMI SE absolute shielding effectiveness(SSE/t)is as high as 39312.78 dB·cm2/g,which remained 99.39%even after 10,000 times repeated folding.These multifunctional ultrathin MXCB sheets can be arranged by vacuum‐assisted induction to develop EM‐IR bi‐stealth sheet.
文摘针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。
