For microelectronic devices,the on-chip microsupercapacitors with facile construction and high performance,are attracting researchers'prior consideration due to their high compatibility with modern microsystems.He...For microelectronic devices,the on-chip microsupercapacitors with facile construction and high performance,are attracting researchers'prior consideration due to their high compatibility with modern microsystems.Herein,we proposed interchanging interdigital Au-/MnO_(2)/polyethylene dioxythiophene stacked microsupercapacitor based on a microfabrication process followed by successive electrochemical deposition.The stacked configuration of two pseudocapacitive active microelectrodes meritoriously leads to an enhanced contact area between MnO_(2)and the conductive and electroactive layer of polyethylene dioxythiophene,hence providing excellent electron transport and diffusion pathways of electrolyte ions,resulting in increased pseudocapacitance of MnO_(2)and polyethylene dioxythiophene.The stacked quasi-solid-state microsupercapacitors delivered the maximum specific capacitance of 43 mF cm^(-2)(211.9 F cm^(-3)),an energy density of 3.8μWh cm^(-2)(at a voltage window of 0.8 V)and 5.1μWh cm^(-2)(at a voltage window of 1.0 V)with excellent rate capability(96.6%at 2 mA cm^(-2))and cycling performance of 85.3%retention of initial capacitance after 10000 consecutive cycles at a current density of 5 mA cm^(-2),higher than those of ever reported polyethylene dioxythiophene and MnO_(2)-based planar microsupercapacitors.Benefiting from the favorable morphology,bilayer microsupercapacitor is utilized as a flexible humidity sensor with a response/relaxation time superior to those of some commercially available integrated microsensors.This strategy will be of significance in developing high-performance on-chip integrated microsupercapacitors/microsensors at low cost and environment-friendly routes.展开更多
Aiming to enhance the bandwidth in near-memory computing,this paper proposes a SSA-over-array(SSoA)architecture.By relocating the secondary sense amplifier(SSA)from dynamic random access memory(DRAM)to the logic die a...Aiming to enhance the bandwidth in near-memory computing,this paper proposes a SSA-over-array(SSoA)architecture.By relocating the secondary sense amplifier(SSA)from dynamic random access memory(DRAM)to the logic die and repositioning the DRAM-to-logic stacking interface closer to the DRAM core,the SSoA overcomes the layout and area limitations of SSA and master DQ(MDQ),leading to improvements in DRAM data-width density and frequency,significantly enhancing bandwidth density.The quantitative evaluation results show a 70.18 times improvement in bandwidth per unit area over the baseline,with a maximum bandwidth of 168.296 Tbps/Gb.We believe the SSoA is poised to redefine near-memory computing development strategies.展开更多
Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the i...Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.展开更多
基金the financial support of the National Key R&D Program of China(Grant Nos.2021YFB3200701 and 2018YFA0208501)the National Natural Science Foundation of China(Grant Nos.21875260,21671193,91963212,51773206,21731001,and 52272098)Beijing Natural Science Foundation(No.2202069)
文摘For microelectronic devices,the on-chip microsupercapacitors with facile construction and high performance,are attracting researchers'prior consideration due to their high compatibility with modern microsystems.Herein,we proposed interchanging interdigital Au-/MnO_(2)/polyethylene dioxythiophene stacked microsupercapacitor based on a microfabrication process followed by successive electrochemical deposition.The stacked configuration of two pseudocapacitive active microelectrodes meritoriously leads to an enhanced contact area between MnO_(2)and the conductive and electroactive layer of polyethylene dioxythiophene,hence providing excellent electron transport and diffusion pathways of electrolyte ions,resulting in increased pseudocapacitance of MnO_(2)and polyethylene dioxythiophene.The stacked quasi-solid-state microsupercapacitors delivered the maximum specific capacitance of 43 mF cm^(-2)(211.9 F cm^(-3)),an energy density of 3.8μWh cm^(-2)(at a voltage window of 0.8 V)and 5.1μWh cm^(-2)(at a voltage window of 1.0 V)with excellent rate capability(96.6%at 2 mA cm^(-2))and cycling performance of 85.3%retention of initial capacitance after 10000 consecutive cycles at a current density of 5 mA cm^(-2),higher than those of ever reported polyethylene dioxythiophene and MnO_(2)-based planar microsupercapacitors.Benefiting from the favorable morphology,bilayer microsupercapacitor is utilized as a flexible humidity sensor with a response/relaxation time superior to those of some commercially available integrated microsensors.This strategy will be of significance in developing high-performance on-chip integrated microsupercapacitors/microsensors at low cost and environment-friendly routes.
基金supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDB44000000。
文摘Aiming to enhance the bandwidth in near-memory computing,this paper proposes a SSA-over-array(SSoA)architecture.By relocating the secondary sense amplifier(SSA)from dynamic random access memory(DRAM)to the logic die and repositioning the DRAM-to-logic stacking interface closer to the DRAM core,the SSoA overcomes the layout and area limitations of SSA and master DQ(MDQ),leading to improvements in DRAM data-width density and frequency,significantly enhancing bandwidth density.The quantitative evaluation results show a 70.18 times improvement in bandwidth per unit area over the baseline,with a maximum bandwidth of 168.296 Tbps/Gb.We believe the SSoA is poised to redefine near-memory computing development strategies.
基金funded by the Natural Science Foundation of Shandong Province (ZR2021MD061ZR2023QD025)+3 种基金China Postdoctoral Science Foundation (2022M721972)National Natural Science Foundation of China (41174098)Young Talents Foundation of Inner Mongolia University (10000-23112101/055)Qingdao Postdoctoral Science Foundation (QDBSH20230102094)。
文摘Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.
文摘为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。
