Measuring code maintainability with deep neural networks

The maintainability of source code is a key quality characteristic for software quality. Many approaches have been proposed to quantitatively measure code maintainability. Such approaches rely heavily on code metrics, e.g., the number of Lines of Code and McCabe’s Cyclomatic Complexity. The employed code metrics are essentially statistics regarding code elements, e.g., the numbers of tokens, lines, references, and branch statements. However, natural language in source code, especially identifiers, is rarely exploited by such approaches. As a result, replacing meaningful identifiers with nonsense tokens would not significantly influence their outputs, although the replacement should have significantly reduced code maintainability. To this end, in this paper, we propose a novel approach (called DeepM) to measure code maintainability by exploiting the lexical semantics of text in source code. DeepM leverages deep learning techniques (e.g., LSTM and attention mechanism) to exploit these lexical semantics in measuring code maintainability. Another key rationale of DeepM is that measuring code maintainability is complex and often far beyond the capabilities of statistics or simple heuristics. Consequently, DeepM leverages deep learning techniques to automatically select useful features from complex and lengthy inputs and to construct a complex mapping (rather than simple heuristics) from the input to the output (code maintainability index). DeepM is evaluated on a manually-assessed dataset. The evaluation results suggest that DeepM is accurate, and it generates the same rankings of code maintainability as those of experienced programmers on 87.5% of manually ranked pairs of Java classes.     

基于高光谱激光雷达的滞尘叶片光谱特征分析

城市绿植为城市生态系统提供自净功能, 起到净化空气以及滞尘降尘等多种环境保护作用, 而滞尘等因素也 会对绿植产生影响。为了研究滞尘对城市绿植叶片光谱特征的影响, 采集了四种常绿绿植 (八角金盘、石楠、香樟和 玉兰) 叶片样本, 使用高光谱激光雷达系统获取高光谱点云数据, 分析了滞尘对叶片光谱特征的影响。分析结果表明: 对于不同种类叶片, 滞尘对可见光波段反射率均有较大影响; 对于同种类叶片, 滞尘对近红外波段的反射率差异影响 较大, 可见光波段的反射率差异为 1.21%∼3.41%, 近红外为 1.76%∼8.49%; 线性四点内插法计算和光谱导数分析表明 滞尘对四种叶片的红边位置无显著影响; 四种叶片的叶面水含量指数 (LWI) 对滞尘的响应程度最小 (均小于 3.7%), 而 比值植被指数 (RVI) 对滞尘的响应程度最大 (除香樟外, 均大于 20.0%), 红边指数 (SDr)、简单比值指数 (SR) 和叶面叶 绿素指数 (LCI) 的响应程度稳定性较差。进一步建立了滞尘植被指数和响应程度的线性相关性拟合模型并进行了检 验, 其中以 LCI 为自变量建立的模型为最稳定拟合模型, 可表示为 y = −1.527x + 0.6597, 决定系数约为 0.88。     

一种激光雷达探测模块控制电路的设计和制作

传统上使用机械旋钮调节光电倍增管 (PMT) 增益的方法不仅存在需要人为依据经验手动操作、准确性差等 弊端, 而且 PMT 增益易受温度影响, 需要根据环境温度动态调整 PMT 的高压, 这些局限性都不利于其在激光雷达系 统中的应用。为了便于对 PMT 进行增益调节并保持 PMT 增益的稳定, 设计了可用于激光雷达系统的 PMT 控制电路 板, 该电路可使用计算机实现增益调节和高压的温度自适应调节, 从而精简了信号探测系统的结构与体积, 提升了信 号的稳定性。本工作采用内置高压电源的 H10721-20 型 PMT, 并基于 STM32 单片机结合数模转换器 (DAC) 和外围 电路完成控制电路板的设计并进行实际制作。进一步对使用电位器调节的 PMT 和利用所提出的控制方法进行调节 的 PMT 进行了高压稳定性的对比实验, 并对使用 PMT 控制电路板的米散射激光雷达的性能进行了测试。实验结果 表明所提出的控制方法可实现更稳定的 PMT 增益控制, 设计的 PMT 控制电路板具有良好的可靠性。     

基于协同强化学习的微电网分布式两级电压优化控制

由于微电网中分布式电源组成复杂,运行模式多样,孤岛微电网的电压恢复控制面临着不确定性干扰的影响。为此,针对不确定性干扰下微电网的二级电压恢复控制问题,提出了一种基于协同强化学习的微电网分布式两级电压优化控制方法,实现孤岛模式下微电网的电压调节控制。首先构建孤岛微电网分布式一致性协同电压控制算法,并建立李雅普诺夫函数稳定性判定方法。其次根据控制器性能与控制器增益参数的关系,求解孤岛微电网电压控制器增益上界,并根据控制器增益参数上界限制强化学习智能体动作集。随后,采用强化学习算法优化二级控制器增益参数,给出相应的强化学习智能体状态集、协同全局奖励函数。最后在Matlab/Simulink上通过仿真实验验证了所提出的控制方法的有效性和适应性。     

Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.     

Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.     

电线电缆带电燃烧研究进展

分析了当前国内外在电线电缆带电燃烧方面的研究进展,主要关注了在役电缆温升特性、带电电缆故障着火机理和电流变量与燃烧特性的相互影响,分别对应了带电电线电缆起火本质原因、引燃特性及火焰蔓延特性。在电缆温升特性方面,分析了以热路模型或者数值计算模型为基础所建立的各类电缆线芯温度预测模型,并介绍了基于电缆温度预测模型所建立的各类线缆温度监测系统。在带电电缆故障着火机理方面,分析了由于绝缘层表面导电路径、高温导致空气电离及绝缘材料热解而形成的电弧故障引发火灾,以及由于短路、过载和接触不良等导致电缆线芯过热引发火灾的机理,并揭示了不同故障之间的相互诱发关系,分析了多种故障综合作用引发火灾的机理。在带电电缆燃烧特性方面,分析了电场与燃烧的相互影响关系与机理,并揭示了外部引燃条件下,通电电流变量对电缆燃烧特性及蔓延速度的影响规律,分析表明在制定电缆安全的相关标准和规范时,需考虑电缆火灾中持续通电电流的影响。     

CeO2掺杂制备Ba0.9Ca0.1Ti1-xSnxO3压电陶瓷的结构及电性能

采用传统的陶瓷烧结技术,通过添加0.15%(摩尔分数)CeO2,在1120℃烧结2h,成功制备了新型无铅压电陶瓷Ba0.9 Ca0.1 Ti1-x Snx O3,并且检测了陶瓷样品的微结构和电性能.XRD显示所有陶瓷样品均具有纯的钙钛矿结构,在室温下为典型的四方相,SEM显示适量添加锡离子可以提高陶瓷致密性.在室温下,锡离子改性的BaTiO3基压电陶瓷在x=0.02处显示了优异的压电、介电和铁电性能(d33=276 pC/N,kp=46%,εr=3678,tanδ=2.4%,Pr=18.2μC/cm2,EC=1.12 kV/mm).这些优异的检测结果证实适当添加锡离子能改善BaT iO3基压电陶瓷的电性能.     

多巴胺@氮化硼-碳纳米管/聚酰亚胺复合气凝胶太阳能蒸发器的制备与性能

利用太阳能蒸发器进行水蒸发是生产清洁用水的重要途径之一。为了提高聚酰亚胺(PI)气凝胶的太阳能蒸发性能,本文通过添加多巴胺改性氮化硼(PDA@BN)和羟基化碳纳米管(CNT),采用四定向冷冻干燥和亚胺化工艺制备了PDA@BN-CNT/PI复合气凝胶。研究了PDA@BN和CNT的加入对气凝胶的形貌结构、润湿性能、太阳能蒸发性能的影响。结果表明：PDA@BN-CNT/PI复合气凝胶不仅具有良好的亲水性和太阳能光热转换能力,而且其独特的低弯曲度管状结构促进了水在气凝胶内部的运输,提高了太阳能蒸发性能。该气凝胶在2 kW/m²光照下的蒸发速率为1.95 kg/(m²·h),并展现出优异的循环使用性能、化学稳定性和高效的污水净化能力。     

g-C3N4/Pb复合材料制备及其在铅炭电池负极材料中的应用

为改善铅炭电池的析氢缺陷,提高电池循环使用寿命,以尿素为前驱体制备层状石墨相氮化碳(g-C₃N₄),并将其作为添加剂制备铅炭电池负极板,以活性炭(AC)为对照,研究了g-C₃N₄结构和添加量对铅炭电池电化学性能的影响。结果表明：g-C₃N₄的加入使析氢反应(HER)得到明显抑制,-1.5 V下1wt%gC₃N₄负极板的析氢电流仅为AC负极板的5%。交流阻抗谱图显示1wt%g-C3N4和AC负极材料阻抗(Rs)为0.19868Ω和1.749Ω。更重要的是1wt%g-C₃N₄负极板比电容比1wt%AC负极板高344%。在5 000 h高倍率部分荷电态(HRPSoC)下的电池循环寿命测试中,加入g-C₃N₄后电池寿命比加入AC提升62%。500次循环后,电池容量保持率仍有70%。g-C₃N₄