High-Efficiency Organic Solar Cells Enabled by Chalcogen Containing Branched Chain Engineering: Balancing Short-Circuit Current and Open-Circuit Voltage,Enhancing Fill Factor

The elaborate balance between the open-circuit voltage (V_OC) and the short-circuit current density (J_SC) is critical to ensure efficient organic solar cells (OSCs). Herein, the chalcogen containing branched chain engineering is employed to address this dilemma. Three novel nonfullerene acceptors (NFAs), named BTP-2O , BTP-O-S , and BTP-2S , featuring different peripheral chalcogen containing branched chains are synthesized. Compared with symmetric BTP-2O and BTP-2S grafting two alkoxy or alkylthio branched chains, the asymmetric BTP-O-S grafting one alkoxy and one alkylthio branched chains shows mediate absorption range, applicable miscibility, and favorable crystallinity. Benefiting from the enhanced π–π stacking and charge transport, an optimal power conversion efficiency (PCE) of 17.3% is obtained for the PM6: BTP-O-S -based devices, with a good balance between V_OC (0.912 V) and J_SC (24.5 mA cm⁻²), and a high fill factor (FF) of 0.775, which is much higher than those of BTP-2O (16.1%) and BTP-2S -based (16.4%) devices. Such a result represents one of the highest efficiencies among the binary OSCs with V_OC surpassing 0.9 V. Moreover, the BTP-O-S -based devices fabricated by using green solvent yield a satisfactory PCE of 17.1%. This work highlights the synergistic effect of alkoxy and alkylthio branched chains for high-performance OSCs by alleviating voltage loss and enhancing FF.     

基于ARPA与AIS的船舶避碰问题

在信息技术高度发达的时代,船舶自动识别系统（AIS）也在逐步发展,为船舶在行驶中的安全提供了可靠的技术支持,是一种较为重要的新型航行设备。AIS和ARPA在船舶碰壁中的有效应用,需要对船舶行驶及岸边船舶信息的自动共享、追踪等,以帮助工作人员掌握船舶行驶状况。文中从船舶在行驶中的碰撞问题出发,对避碰问题进行了分析,对其在实际使用中的覆盖面及追踪信息的可靠性进行了研究、对比,从而对比两者的优缺点,并提出了相应的对策。     

单边限幅参与的并网VSC系统次同步振荡近似分析

针对电力电子设备引起的次同步振荡问题,现有研究集中在无法计及限幅的小信号模型分析或考虑限幅对称的近似分析,但极少考虑不对称的单边限幅的影响。针对不对称的单边d轴电流限幅(AU-d-CL,或上电流饱和限幅)参与的并网电压源换流器(voltage source converter,VSC)系统的次同步振荡现象,采用单边限幅的描述函数和广义Nyquist判据进行近似分析。具体地,首先,建立了并网VSC系统的简化模型,分析了其小扰动稳定性,并分析了AU-d-CL参与的负阻尼振荡现象。其次,给出了VSC系统的频域模型和AU-d-CL的描述函数。最后,结合AU-d-CL的描述函数和广义Nyquist判据,近似计算了不同参数和限幅值时的振荡幅值和频率,结果表明计算结果和仿真结果基本一致,可解释次同步振荡在限幅值改变时,频率基本不变,振荡幅值变化的现象。     

表贴式永磁同步电机模型预测转矩控制系统预测模型研究

不同的磁链和转矩预测模型使得表贴式永磁同步电机(SPMSM)模型预测转矩控制(MPTC)系统的性能有所差异。因此建立了基于转子磁链坐标系、定子磁链坐标系和静止坐标系的SPMSM MPTC系统,并进行仿真验证和对比分析。仿真结果为SPMSM MPTC的预测模型的选择提供参考。     

Frequency Offset Estimation Algorithm of High-Order M-APSK Modulation Signal Based on DFT

How to accurately compensate the Doppler shift is the main challenge for broadband satellite terminals. In this paper, a frequency offset estimation algorithm based on frequency domain window function iterative peaking search is proposed with high-order M-APSK signal models, considering both algorithm complexity and estimation accuracy. The variance of the new algorithm is derived mathematically, and performance curve compared with Cramer-Rao Lower Bound (CRLB) is also simulated under various SNR (signal-to-noise ratio). The effectiveness of new method is verified by simulations.

     

基于EEMD和LeNet-5的脑电信号情绪识别

随着科学技术的进步,人们对情绪这一概念有了全新的认识,从过去认为情绪来源于“心”逐渐发展到了当下普遍认为情绪来源于“脑”。针对脑电信号所具有的诸多特性,首先通过去除心电、肌电噪声,滤波提取脑电信号中的有用波段;再利用集合经验模态分解算法(Ensemble Empirical Mode Decomposition,EEMD)对脑电信号进行特征提取,利用提取特征通过空间插值法绘制脑电地形图;接着利用LeNet-5算法开展具体情绪识别,并建立模型。最终通过不断地改进模型,显著提高了情绪识别准确率,准确率最高可达80.1%。     

Occluded Video Instance Segmentation: A Benchmark

International Journal of Computer Vision - Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset...     

无模板剂水热合成Bi12O17Cl2纳米带及其阻燃性能研究

以β-Bi2O3为原料,采用无模板剂水热法制备了Bi_(12)O_(17)Cl_2纳米带,利用X射线衍射(XRD)、扫描电镜(SEM)、透射电镜(TEM)和高分辨透射电镜(HRTEM)对其形貌和结构进行了表征并作为阻燃剂添加到聚氯乙烯(PVC)中评价其阻燃性能。结果表明,适宜的Bi/Cl物质的量比对水热合成Bi_(12)O_(17)Cl_2纳米带及其化学计量比影响显著,当Bi/Cl物质的量比为1∶2.5时,可得到分散性好、形貌均匀的Bi_(12)O_(17)Cl_2纳米带;改变Bi/Cl物质的量比,纳米带中Bi/Cl化学计量比从6∶1变成2.4∶1。初步探讨了Bi_(12)O_(17)Cl_2纳米带可能的生长机理,在水热反应过程中,Bi 3+与Cl-发生络合反应形成BiCln3-n络合物,从而合成Bi_(12)O_(17)Cl_2纳米带。     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

反应过程中升华型催化剂的物性变化(Ⅲ)Zn(Ac)_2 的程序升温脱附测定

用程序升温脱附法测定了乙炔在衰化程度不同的醋酸锌催化剂上的脱附峰面积。发现乙炔在纯载体活性炭上的脱附峰面积为零,证明所得峰面积表征了乙炔在醋酸锌上的化学吸附量。衰化程度不同的催化剂上乙炔的程序升温脱附峰面积与其残留量呈线性关系,证明该催化剂失活的主要原因是活性组分醋酸锌的升华流失造成的。