浅谈煤与瓦斯突出矿井"U"型通风系统采煤工作面上隅角瓦斯管理

屯兰煤矿是煤与瓦斯突出矿井,瓦斯涌出量大,上隅角瓦斯治理困难。为解决这一通风难题,该矿采取沿空留巷抽采措施进行作业。实践证明,该技术解决了上隅角瓦斯积聚隐患,保障了矿井高产高效。     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

基于数值微分核脉冲信号数字处理方法

核反应堆核测量系统测量探测器输出的核脉冲信号，该信号后沿拖尾很长，在计数率较高时容易产生信号堆积和基线漂移等问题，导致源区计数率测量上限仅能达到105 Hz左右。文中基于数值微分方法，采用数字处理技术，在时域上分析了核脉冲信号经过前置放大、信号成形、低通滤波和脉冲甄别后的输出，并利用探测器实测信号进行了仿真。仿真结果表明，基于数值微分的数字处理方法可以实现相邻0.4 μs脉冲信号的识别和测量，将源区测量计数率上限提升到2×106 Hz以上。      

The benefits of integrated eye tracking with airborne image recorders in the flight deck: A rejected landing case study

Aircraft accident investigation has played a pivotal role in improving the safety of aviation. Advances in recorder technology, specifically Cockpit Voice Recorders (CVRs) and Flight Data Recorders (FDR) have made a huge contribution to the understanding of occurrences for accident investigators. However, even these recorders have limitations such as the evidence they provide about pilots' situation awareness or behaviours. Supplementing audio and data recordings with video has been discussed for many years and whilst there continues to be debate among regulators, operators, manufacturers and pilot unions, the International Civil Aviation Organization (ICAO) has mandated Airborne Image Recorders (AIR) from 2023. The purpose of installing such systems is to provide evidence of crew operational behaviours in terms of both human-human and human-computer interactions (HCI) on the flight deck. Video alone is unlikely to provide sufficient evidence for investigators. This study examines the additional value that eye-tracking technology may provide through the case study of an accident involving an Airbus A330-300 aircraft which experienced a rejected landing. Currently, the investigation of such events, where crew interaction with automation is critical to their situation awareness, relies heavily on interview data. Such data may be unavailable (in the case of serious injury) or unreliable (based on hindsight bias). By integrating eye tracking technology into an AIR, accident investigators will potentially gain a better understanding of pilots’ visual scan patterns across flight deck instrumentation. This has implications for flight deck and procedural design as well as training and simulation.     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.     

内插螺旋立式上行管流场及传热性能的实验研究

通过粒子图像测速流场实验与传热实验相结合，研究了内插螺旋立式上行管的螺旋节距、丝径、中径比等结构参数在不同Re下对流场、阻力及传热性能的影响。结果表明，内插螺旋能够有效扰动和混合管内流体，使管内形成多个纵向旋涡的流体结构、增大管壁附近液体涡量，有利于强化传热。当Re相同时，管内平均流速v、Nu和综合换热性能PEC均随丝径增大而增大，随中径比减小而增大；随节距增大，3种参数均出现增大的趋势，节距大于20 mm后开始减小。管内流体的阻力f随丝径和节距增大而减小，随中径比增大而增大。综合比较，在较低Re时，节距p=20 mm、丝径e=1.6 mm、中径比D/d=0.75时综合传热效果最好。     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.