基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

Experimental study on the propagation characteristics of hydrogen/methane/air premixed flames in a narrow channel

This work is focused on the explosion characteristics of premixed gas containing different volume fractions of hydrogen in a narrow channel (1000 mm × 50 mm × 10 mm) under the circumstance of stoichiometric ratio. The ignition positions were set in the closed end and the middle of the pipeline respectively. The results showed that when the gas was ignited at the pipeline closed end, the propagating flame was tulip structure for different premixed gas. When the hydrogen volume fraction was less than 40%, the flame propagation speed increased significantly with the rise of hydrogen volume fraction, and the overpressure peak also appeared obviously in advance. However, when the volume fraction of hydrogen was more than 40%, the increase of flame propagation speed and the overpressure peak occurrence time varied slightly. Furthermore, when the ignition position was placed in the middle of the pipeline, the flame propagation speed propagating to the opening end was much faster than that propagating to the closing end, and there was no tulip shape when the flame propagates to the opening end. The flame propagating to the closed end appeared tulip shape under the influence of airflow, and high-frequency flame oscillation occurred during the propagation. This work shows that the hydrogen volume fraction and ignition position significantly affected the flame structure, flame front speed, and explosion overpressure.     

基准平面垂直断面法在爆破漏斗试验中的应用

系统阐述了基准平面垂直断面法在爆破漏斗试验中测量爆破漏斗体积的基本原理,并将隧道激光断面仪应用于金厂河矿1 750 m水平15^#采场底部切割巷道爆破漏斗试验爆破漏斗体积测量中。通过与传统体重法等计算法所得漏斗体积分析比较,结果表明基于隧道激光断面仪与3D Mine软件分析的基准平面垂直断面法实用性强、操作方便、结果直观可靠,达到试验预期目的。     

急性炎性疾病患者乳酸林格氏液液体动力学

目的:探索炎性疾病患者的乳酸林格氏液（Ringer's lactate,RL）液体动力学特征以及炎性生物标记物是否可以作为协变量影响RL分布和排泄。方法:本研究为前瞻性队列研究。选择40例美国麻醉医师分级（ASA）I-II级，腹腔镜下择期胆囊切除术（胆囊炎组，n=20）或者腹腔镜下急诊阑尾切除术（阑尾炎组，n=20）。所有患者麻醉诱导前开始输注RL，按15 mL/kg，35 min内输毕。采用酶联免疫（enzyme-linked immunosorbent assay,ELISA）方法测定血浆炎症（TNF-α,IL-10和CRP）或者内皮损伤生物标记物（syndecan-1,SDC-1）；利用血红蛋白（Hb）稀释-时间曲线和尿量，使用Phoenix软件，采用非线性混合效应模型分析计算RL液体动力学参数和协变量的影响。结果:与胆囊炎组相比，阑尾炎组RL从组织间隙到血浆的转运速率常数（k21）显著降低（14×10-3min-1 versus 35×10-3min-1;P=0.012）。阑尾炎组C反应蛋白（CRP）升高［中位数38.1(1.8-143.6) μg/mL versus 1.3(0.1-159.0) μg/mL;P<0.001］；与清醒状态相比，麻醉期间（输液开始后30～45 min），液体从中央室中到外周室的转运速率常数（k12）显著增加（57×10-3min-1 versus 32×10-3min-1；P<0.01）。清除速率常数（k10）降低90%（0.6×10-3min-1 versus 5.3×10-3min-1;P<0.001）。无论在清醒状态还是麻醉状态下低血压均能降低液体清除；炎症或者内膜损伤的生物标记物不能作为显著影响RL液体动力学参数的协变量。结论:阑尾炎或者胆囊炎患者术前输入液体后“炎症反应的生物标记物”不是RL的液体动力学的协变量，但是两组患者中，全身麻醉期间输入液体的清除率下降。     

Design and experimental investigation of the high efficiency 1.5 W/4.2 K pneumatic-drive G-M cryocooler

The development of a high cooling power and high efficiency 4.2 K two stage G-M cryocooler is critically important given its broad applications in low temperature superconductors, MRI, infrared detector and cryogenic electronics. A high efficiency 1.5 W/4.2 K pneumatic-drive G-M cryocooler has recently been designed and developed by ARS. The effect of expansion volume rate and operation conditions on the cooling performance has been experimentally investigated. A typical cooling performance of 1.5 W/4.2 K has been achieved, and the minimum temperature of the second stage is 2.46 K. The steady input power of the compressor at 60 Hz is 6.8 kW, while the operation speed of the rotary valve is 30 rpm. A maximum cooling power of 1.75 W/4.2 K has been obtained in test runs.     

面部运动单元检测研究综述

面部运动单元检测旨在让计算机从给定的人脸图像或视频中自动检测需要关注的运动单元目标。经过二十多年的研究，尤其是近年来越来越多的面部运动单元数据库的建立和深度学习的兴起，面部运动单元检测技术发展迅速。首先，阐述了面部运动单元的基本概念，介绍了已有的常用面部运动单元检测数据库，概括了包括预处理、特征提取、分类器学习等步骤在内的传统检测方法；然后针对区域学习、面部运动单元关联学习、弱监督学习等几个关键研究方向进行了系统性的回顾梳理与分析；最后讨论了目前面部运动单元检测研究存在的不足以及未来潜在的发展方向。     

基于RFID标签阵列的睡眠期间呼吸量连续监测系统

睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测,缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据,计算出呼吸引起的胸部不同点的位移量,基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型,从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签,消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明,RFSLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。