基于IDE-BPNN的快速反射镜迟滞建模

由压电陶瓷驱动的快速反射镜(FSM)现已被广泛用于自适应光学系统的执行环节,为了对其迟滞效应精确建模,该文提出了一种针对FSM的IDE-BPNN建模方法。基于Madelung法则以最小二乘法构建称迟滞算子作为迟滞运动的基本描述,扩展训练用的数据集,并采用改进的差分进化算法(IDE)对BP神经网络(BPNN)进行训练。实验表明,当输入30 Hz衰减的正弦信号时,IDE-BPNN模型的单轴最大误差为0.745μrad,归一化最大误差为0.87%,归一化均方根误差为0.36%。相较于最小二乘建模法,相对于最小二乘模型误差大幅缩小,有较好的使用价值。     

考虑车主多模式需求响应模糊意愿的优化调度策略

现代电动汽车(electrical vehicle, EV)用户需求响应具有多样性和意愿模糊性的特点,当实施单一激励政策时,EV响应将达不到预期效果。为此,提出了考虑车主多模式需求响应及其模糊意愿的含EV微电网的主从博弈优化调度策略。微电网主体针对净负荷制定多模式动态电价激励政策,引导EV在多模式电价中做出选择,促进EV有序充放电,实现其净负荷均方差和运行成本最小。车主从体基于模糊逻辑推理意愿决策,响应多模式动态电价,极小化车主成本。采用非支配排序遗传算法(NSGA-Ⅱ)求解优化模型,获得最优多模式动态电价和EV充放电策略。仿真结果验证了所提方法的有效性。     

A multi-energy inertia-based power support strategy with gas network constraints

An integrated energy system with multiple types of energy can support power shortages caused by the uncertainty of renewable energy. With full consideration of gas network constraints, this paper proposes a multi-energy inertia-based power support strategy. The definition and modelling of gas inertia are given first to demonstrate its ability to mitigate power fluctuations. Since partial utilization of gas inertia can influence overall gas network parameters, the gas network is modelled with an analysis of network dynamic changes. A multi-energy inertia-based power support model and strategy are then proposed for fully using gas-thermal inertia resources in integrated energy systems. The influence of gas network constraints on strategy, economy and power outputs is analyzed. Special circumstances where the gas network can be simplified are introduced. This improves the response speed and application value. The feasibility and effectiveness of the proposed strategy are assessed using a real scenario.     

Promote Intratumoral Drug Release and Penetration to Counteract Docetaxel-Induced Metastasis by Photosensitizer-Modified Red Blood Cell Membrane-Coated Nanoparticle

The red blood cell membrane (RBCm) provides tight protection, lowers the immunogenicity, and prolongs the circulation time of drugs in vivo when acting as the coating of drug delivery systems. However, the cellular uptake and release of drugs are hindered by RBCm. Docetaxel (DTX) is the first-line medicine for treating triple-negative breast cancer (TNBC), but it induces tumor metastasis. To solve these dilemmas, in this study, the photosensitizer 1,1-dioctadecyl-3,3,3,3-tetramethylindotricarbocyanine iodide (DiR)-modified RBCm (DM) is prepared, which is coated onto a hybrid micelle consisting of the prodrugs of DTX and the anti-metastasis agent calcitriol (CTL), obtaining a nanoparticle, named HDC-DM. In a 4T1 tumor-bearing mouse model, after injecting HDC-DM, the intratumoral DTX and CTL concentrations are increased by 1.7 and 2.5 times compared with the free drug groups. After irradiating tumors with near-infrared laser, DiR elicits the photothermal effect, triggering the rupture of RBCm and drug release, promoting drug penetration in tumors, and inducing immunogenic cell death. The tumor growth inhibition rate is 77%, and the formation of lung metastases is reduced by 82%, with good biocompatibility. It is suggested that the combination of phototherapy, chemotherapy, and anti-metastatic therapy using HDC-DM is expected to be a powerful strategy for treating TNBC.     

美欧高功率微波技术研究现状及发展趋势

当前,随着无人机集群等新型作战武器的逐步实战化,作为其反制手段的高功率微波等定向能武器受到了高度关注。美欧各军事大国均大力推进高功率微波技术研究及武器装备研发,部分高功率微波武器装备已经完成了概念演示验证,正逐步形成实战化装备。从高功率微波武器关键技术研究和武器装备发展2个方面,对高功率微波领域研究现状进行了梳理,重点介绍了脉冲功率驱动源技术、高功率微波产生技术和高功率微波辐射技术方向的研究进展,总结了典型高功率微波武器装备的性能特点,并分析了其发展趋势。     

Challenges and Opportunities of Implantable Neural Interfaces: From Material,Electrochemical and Biological Perspectives

The desirable implantable neural interfaces can accurately record bioelectrical signals from neurons and regulate neural activities with high spatial/time resolution, facilitating the understanding of neuronal functions and dynamics. However, the electrochemical performance (impedance, charge storage/injection capacity) is limited with the miniaturization and integration of neural electrodes. The “crosstalk” caused by the uneven distribution of elctric field leads to lower electrical stimulation/recording efficiency. The mismatch between stiff electrodes and soft tissues exacerbates the inflammatory responses, thus weakening the transmission of signals. Though remarkable breakthroughs have been made through the incorporation of optimizing electrode design and functionalized nanomaterials, the chronic stability, and long-term activity in vivo of the neural electrodes still need further development. In this review, the neural interface challenges mainly on electrochemistry and biology are discussed, followed by summarizing typical electrode optimization technologies and exploring recent advances in the application of nanomaterials, based on traditional metallic materials, emerging 2D materials, conducting polymer hydrogels, etc., for enhancing neural interfaces. The strategies for improving the durability including enhanced adhesion and minimized inflammatory response, are also summarized. The promising directions are finally presented to provide enlightenment for high-performance neural interfaces in future, which will promote profound progress in neuroscience research.     

Unsupervised model-driven neural network based image denoising for transmission line monitoring

With the expansion of smart grid and Internet of things (IoT) technology, edge computing has a wide variety of applications in these domains. The criteria for real-time monitoring and accuracy are particularly high in the field of online real-time monitoring of electricity lines. Based on edge technology, high-quality real-time monitoring can be performed for transmission lines using image processing techniques. Therefore, we propose an image denoising method, which can learn clean images using a stream-based generative model. The stream model uses a two-stage approach in the network to handle the different training periods of denoising separately. Experimental results show that the proposed method has good denoising performance.     

基于用户行为的社交网络人格特质识别方法

社交网络已成为人们获取和发布信息的一个重要平台,也是黑客发起网络诈骗的主要场地。大多数黑客在发起网络诈骗之前,首先会判别目标用户的主要人格特点,然后根据主要人格特点制定与其接触的策略。因此,面向社交网络用户的人格特质识别方法的研究对提高用户识别社交网络诈骗能力具有重要意义。提出基于用户的人格特质识别方法。通过构建面向社交网络的人格特质词典提取用户发表或转发文本信息中能反映用户主要人格特质类型的观测值,采用5个具有不同参数值的隐半马尔可夫模型刻画用户在社交网络上发表或转发文本信息的行为过程。在人格特质识别阶段,通过计算每个用户在发表或转发文本信息过程中产生的观测序列相对于模型的平均对数似然概率,以识别用户所属的人格特质类型。在采集的新浪微博数据集上进行实验,结果表明,当假正率为10%时,该方法的总真正率为93.18%,能准确识别用户的人格特质类型。     

Hydrofluoric Acid-Removable Additive Optimizing Electrode Electrolyte Interphases with Li+ Conductive Moieties for 4.5 V Lithium Metal Batteries

High-voltage lithium metal batteries (LMBs) are capable to achieve the increasing energy density. However, their cycling life is seriously affected by unstable electrolyte/electrode interfaces and capacity instability at high voltage. Herein, a hydrofluoric acid (HF)-removable additive is proposed to optimize electrode electrolyte interphases for addressing the above issues. N, N-dimethyl-4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl) aniline (DMPATMB) is used as the electrolyte additive to induce PF₆⁻ decomposition to form a dense and robust LiF-rich solid electrolyte interphase (SEI) for suppressing Li dendrite growth. Moreover, DMPATMB can help to form highly Li⁺ conductive Li₃N and LiBO₂, which can boost the Li⁺ transport across SEI and cathode electrolyte interphase (CEI). In addition, DMPATMB can scavenge traced HF in the electrolyte to protect both SEI and CEI from the corrosion. As expected, 4.5 V Li|| LiNi_0.6Co_0.2Mn_0.2O₂ batteries with such electrolyte deliver 145 mAh g⁻¹ after 140 cycles at 200 mA g⁻¹. This work provides a novel insight into high-voltage electrolyte additives for LMBs.     

Grafted MXenes Based Electrolytes for 5V-Class Solid-State Batteries

Polymer blends based solid polymer electrolytes (SPEs), combining the advantages of multiple polymers, are promising for the utilization of 5 V-class cathodes (e.g., LiCoMnO₄ (LCMO)) with enhanced safety. However, severe macro-phase separation with defects and voids in polymer blends restrict the electrochemical stability and ionic migration of SPEs. Herein, inorganic compatibilizer polyacrylonitrile grafted MXene (MXene-g-PAN) is exploited to improve the miscibility of the poly(vinylidene fluoride-co-hexafluoropropylene) (PVHF)/PAN blends and suppress the consolidation of phase particles. The resulting SPE exhibits a high anodic stability with an ionic conductivity of 2.17 × 10⁻⁴ S cm⁻¹, enabling a stable and reversible Li platting/stripping (over 2500 h). The fabricated solid Li‖LCMO cell delivers a 5.1 V discharge voltage with a decent capacity (131 mAh g⁻¹) and cycling performance. Subsequently, the solid all-in-one graphite‖LCMO battery is also constructed to extend the application of MXene based SPEs in flexible batteries. Benefiting from the interface-less design, outstanding mechanical flexibility and stability is achieved in the battery, which can endure various deformations with a low-capacity loss (< ≈10%). This study signifies a significant development on solid flexible lithium ion batteries with enhanced performance, stability, and reliability by investigating the miscibility of polymer blends, benefiting for the design of high-performance SPEs.