基于线状特性的大数据地铁用户识别与定位方法

从MR原始数据特性出发研究数据结构与字段,采用基于线状特性分析MR大数据的地铁用户识别过滤方法和位置定位方法,该方法经过验证能够有效输出地铁用户数据并定位在地图上,解决人工地铁测试效率低且测试片面的问题,实现基于大数据的自动化地铁网络评估,提供海量的有效定位数据用于地铁评估与分析。     

Structure of presented stimuli influences gazing behavior and choice

Well-structured stimuli presentation is essential in eye-tracking research to test predefined hypotheses reliably and to conduct relevant gazing behavior studies. Several bottom-up factors associated with stimuli presentation (such as stimuli orientation, size etc.) can influence gazing behavior. However, only a small number of scientific papers address these factors in a sensory and consumer science context and thus provide guidance to practitioners. The two presented eye-tracking studies on food images aimed at evaluating the effect of the bottom-up factors stimulus size, background of the picture, orientation of food product presentation, the evaluated products and the number of alternatives. Significant effects of product group were found in the case of all eye-movement parameters except time to first fixation and first fixation duration. In contrary, orientation significantly influenced only the time to first fixation and first fixation duration parameters. Stimulus size significantly increased fixation and dwell count, while background showed no significant effects. Furthermore, significant relationships were found between the number of presented images and eye-movement and decision time. Less time was needed in 2AFC (alternative forced choice test), 3AFC and 4AFC and significantly more time was needed to choose one alternative out of 7AFC and 8AFC. The results of the two studies show that the investigated bottom-up factors can significantly influence gazing behavior, and therefore need to be carefully considered when planning or comparing results of eye-tracking experiments.     

采用磁流变阻尼器的人体座椅悬架系统模糊ANFIS-PID复合控制技术研究

对座椅悬架用单出杆式磁流变阻尼器进行阻尼特性试验,并借助MATLAB多项式拟合工具箱对改进多项式模型中未知参数进行辨识。分析人体振动特性,建立五自由度人体座椅悬架模型。综合模糊控制器与自适应模糊神经推理系统(Adaptive Neuro-Fuzzy Inference System,ANFIS)整定PID控制器的优点,提出一种模糊ANFIS-PID复合控制策略。采用正弦信号作为外界激励,分别对被动悬架、传统模糊控制、ANFIS-PID控制及模糊ANFIS-PID复合控制人体座椅悬架系统进行仿真分析。结果表明,辨识得出的参数和建立的改进多项式模型均可满足后续仿真要求;采用模糊ANFIS-PID复合控制策略的隔振效果明显优于传统模糊控制及ANFIS-PID控制,能有效改善人体座椅悬架系统的行驶平顺性及驾乘人员乘坐舒适性。     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

A tutorial on the segmentation of metallographic images: Taxonomy,new MetalDAM dataset,deep learning-based ensemble model,experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.     

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.