基于YOLOv3的船舶目标检测算法

为提高船舶目标智能检测的精度和实时性,提出一种基于YOLOv3算法的船舶目标检测方法,可用于视频图像的监测与跟踪。参照PASCAL VOC数据集格式,构建船舶目标检测数据集,采用k-means聚类先验框、mixup、标签平滑化等方法对算法进行改进和优化,在GPU(Graphic Processing Unit)云服务器中完成算法模型的训练和检测,并与FasterR-CNN、SSD(Single Shot MultiBox Detector)、原始YOLOv3等算法进行模型性能的试验对比。试验结果表明:改进的算法明显优于其他算法,其在测试集上的平均精度均值(mean Average Precision,mAP)和检测速度分别达到89.90%和30每秒检测帧数(Frames Per Second,FPS)。     

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.     

脑尔康对老年痴呆小鼠脑内胆碱酯酶活性及神经元的影响

目的　通过慢性铝中毒造成老年痴呆小鼠模型。观察复方中药脑尔康对小鼠学习记忆障碍的保护作用 ,探讨其对胆碱酯酶活性的影响及保护神经元的抗痴呆作用机理。方法　健康昆明种雄性小鼠 60只 ,随机分为 6组 ,除空白组外 ,连续造模 50d后 ,每组随机取 3只 ,用HE染色法 ,观察小鼠海马CA1区细胞数量及形态 ,其余动物迅速断头取脑 ,冰台分离海马及皮层 ,检测各自的胆碱脂酶活性。结果　模型组AchE活性明显增高 (P <0 0 1 ) ,用药各组与模型组比较 ,除小剂量组外AchE活性均明显低于模型组 (P <0 0 1 ) ;模型组海马CA1区神经元数量明显少于空白组 (P <0 0 1 ) ,可见神经元脱失 ,并有胶质细胞增生。用药各组与模型组比较 ,只有中药大、中剂量组神经元数量显著多于模型组 (P <0 0 5或P <0 0 1 )。结论　脑尔康能明显降低老年痴呆模型小鼠皮层及海马AchE活性 ,保护海马CA1区神经元的作用。     

用新陈代谢极限学习机实现电池健康状态估算

针对因电池内部电化学反应的复杂性、算法泛化性差或可用已知数据量少导致的锂离子电池SOH估算精度下降的问题,提出使用极限学习机(ELM)构建强泛化性电池退化状态模型来描述不同电池的共性退化规律;引入新陈代谢机制来更新退化状态模型的输入数据进而实现对SOH的新陈代谢估算,在保证估算精度的同时降低对输入数据量的需求.利用两种...     

高职高专院校金工实训教学改革的探索与实践

根据笔者多年金工实训教学实践,文中论述了广东交通职业技术学院从中专学校升为高职高专院校后对金工实训教学方法进行的改革性探索与实践。     

基于深度学习的车辆轨迹重建与异常轨迹识别

车辆移动轨迹的不确定性及异常点段的存在使其在数字交通领域的应用面临挑战.本文构建基于数据增强的LSTM-AE-Attention深度学习模型,进行车辆轨迹重建和异常轨迹识别.首先,使用对抗生成网络和贝塞尔样条曲线从样本量和种类两方面扩充数据集,实现数据增强;其次,通过自编码网络与长短时记忆神经网络提取轨迹特征并完成轨迹...     

高职高专金工实习教学改革探索

文中论述了近机类高职高专院校金工实习教学改革的目标、任务,探讨了金工实习的教学模式和改革思路并制定了实施方案,有利于培养学生的实践能力及创新能力。     

课程思政视域下高职数学课程学习效果评价途径探究——以概率论在核酸检查中的作用为例

“立德树人”是教育的根本任务.在课程思政视域下,高职数学课程的教学结构整合与优化、教学方式与方法的改革、学生学习习惯与方式的改变,都对高职数学课程学习效果评价提出了新的要求.本文从对学生知识、能力、素质目标考核中探究出一系列关于高职数学课程学习效果评价的途径与方法,明确高职数学课程的育人核心内涵,更全面地完善了学生终身...     

ENDPOINT DETECTOR OF NOISY SPEECH SIGNAL USING A RECURRENT NEURAL NETWORK

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