加强应用化学专业学生实践能力培养的改革与实践

实践教学是培养应用化学专业创新型人才的一个重要途径,通过构建“一体化、三层次”的实验教学体系和“三阶段”、“三结合”的实习模式,有利于从根本上解决学校教育与社会需求不能很好衔接的问题,切实增强学生的综合实践能力,使应用化学专业更好的服务天津经济社会发展．     

天津市战略性新兴产业的培育对策研究

培育和发展战略性新兴产业是推进产业结构升级,加快经济增长方式转变,实现经济社会可持续发展的必由之路.为此,要通过合理定位、科学选择重点产业和优先领域,建立健全政策体制,完善法律法规建设,营造良好发展环境;应强化产学研合作,构建完善的科技中介服务体系和多渠道的激励机制,加大重大科技成果转化力度,推动天津战略性新兴产业的发展.     

海河、永定新河、独流减河三河口 生态需水量研究

保证河口一定规模的生态用水需求,是恢复河口生态系统健康的重要因素。依据沈焕庭的河口定义,参考有关文献水文模型界定了海河、永定新河及独流减河河口区域。据此计算了永定新河、海河、独流减河河口的适宜生态需水量。与多年入海径流量及文献数据对比,永定新河、独流减河口的生态需水量计算结果较为合理;但海河口的计算结果明显偏低,其主要原因是海河口开发利用强度大,填海造陆,原有河口海域被大量侵占,水域面积大量减少等等。     

改性纳米金刚石液压导轨油

介绍了所研制的CLH 0150型改性纳米金刚石液压导轨油。与市售的顶级国际知名品牌1409型液压导轨油相比具有显著的优越性。当Pr/Ps=0.4时，滑轨流阻系数Rh高出12-25%。当负荷为1400kg时，静压滑轨的面积系数α1约高18%。当负荷为740kg时，劲度系数Se可高出20%以上。使用该油，可使滑轨油膜承载能力提高，防爬行能力增强，从而提高加工精度。     

Non-Volatile Electrolyte-Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low-Power Neuromorphic Computing and Logic-In-Memory

Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS₂-based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS₂-based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm⁻²), and long retention characteristics (>10⁴ s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G_max/G_min ratio (10³), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the non-volatile characteristics of the GDY/MoS₂-based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS₂-based EGT for next-generation low-power electronics beyond von Neumann architecture.     

An Ultrafast Nonvolatile Memory with Low Operation Voltage for High-Speed and Low-Power Applications

Memory plays a vital role in modern information society. High-speed and low-power nonvolatile memory is urgently demanded in the era of big data. However, ultrafast nonvolatile memory with nanosecond-timescale operation speed and long-term retention is still unavailable. Herein, an ultrafast nonvolatile memory based on van der Waals heterostructure is proposed, where a charge-trapping material, graphdiyne (GDY), serves as the charge-trapping layer. With the band-engineered heterostructure and excellent charge-trapping capability of GDY, charges are directly injected into the GDY layer and are persistently captured by the trapping sites in GDY, which result in an ultrafast writing speed (8 ns), a low operation voltage (30 mV), and a long retention time (over 10⁴ s). Moreover, a high on/off ratio of 10⁶ is demonstrated by this memory, which enables the achievement of multibit storage with 6 discrete storage levels. This device fills the blank of ultrafast nonvolatile memory technology, which makes it a promising candidate for next-generation high-speed and low-power-consumption nonvolatile memory.     

Simultaneous displacement measurement method of multiple targets based on laser self-mixing interference

In order to simultaneously measure the displacements of multiple targets, the laser self-mixing interference(SMI) measurement method based on variational mode decomposition(VMD) is proposed. The SMI signal containing the motion information of the external targets is detected by a photodiode. The VMD can non-recursively decompose the mixed SMI signal into SMI signals corresponding to different external targets. The displacement signals can be reconstructed by fringe counting method and interpolation method. The experimental results show that the displacement signals of different external targets can be reconstructed with half wavelength accuracy, which verifies the correctness and feasibility of this method. This method can improve the measurement efficiency, which offers an effective method for the multi-channel displacement measurement.     

WB-KNN for emotion recognition from physiological signals

K-nearest neighbor (KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues:the majority vote only by the nearest neighbors is too simple to deal with complex (like skewed) class distribution; features with the same contribution to the similarity will degrade the classification accuracy; samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight (both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.     

基于深度学习的视觉SLAM闭环检测方法

针对在摄像机视角、光照、气候、地貌等条件的大 幅度变化或者存在快速移动物体的 复杂场景下,视觉即时定位与地图构建(simultaneous localization and mapping,SLAM)的精确性和鲁棒性较低等问题,闭环检 测作为解决SLAM位姿漂移的重要环节,提出了一种基于神经网络的闭环检测方法。该方 法通过传感器获取视觉图像的数据,不同于传统方法的特征提取,采用改进三重约束损 失函数训练Darknet提取图像特征,构造对应特征向量矩阵。由于Darknet借鉴了残差网络(resnet)的思想,在具有较深网络层数的同时,仍保持较高的准确率,减少了特征提取 误差。经过自编码器方法对数据进行降维处理,通过余弦相似度计算,设定合理阈值,能够 更快的得到闭环检测结果。最后通过在两个公开视觉SLAM闭环检测数据集,New College数 据集和光照及角度变化更明显的City Centre数据集上进行实验,结果表明复杂环境下本文 提出的方法比现有闭环检测方法,能够得到更高准确率和速率,更好满足了视觉SLAM系统对 消除累计误差和实时性的要求。     

新型高掺铥硅酸盐玻璃光纤及其光纤激光的研究

具有高稀土离子掺杂浓度的有源光纤一直以来是高性能单频光纤激光器的核心,选用硅酸盐玻璃材料制作高掺杂有源光纤可以有效提升光纤增益。通过高温熔融工艺制备了铥离子掺杂浓度为8 wt.%的高掺杂硅酸盐玻璃,测试了其光谱特性和荧光寿命,并根据McCumber理论计算玻璃的受激发射截面。采用管棒法制备光纤预制棒,拉制出尺寸为7/125 μm的高掺铥硅酸盐玻璃光纤。基于低损耗的异质光纤熔接,测试了该光纤的增益特性,并分别采用2 cm和8 cm的新型掺铥光纤搭建线形腔光纤激光器,获得百毫瓦的1950 nm激光输出。研究表明,在8 wt.%的高浓度掺杂下,铥离子在文中的新型硅酸盐光纤基质中具备良好的发光能力,这种国产高掺杂玻璃光纤在实现高性能单频光纤激光器方面具有明显优势。