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%。     

Evaluation of cut cell cartesian method for simulation of a hook and claw type hydrogen pump

Hook and claw pumps are used for recirculation of excess hydrogen in fuel cells. Optimization of the pump design is essential. Computational Fluid Dynamic (CFD) is an effective method for performance optimization. However, it is difficult to conduct CFD simulation because of the sharp cusp of the rotor profile. Cut cell Cartesian mesh could be the solution to handle this complex and moving geometries. The aim of this paper is to evaluate ANSYS Forte for hook and claw pumps. Firstly, the conservation accuracy of the cut cell cartesian mesh is verified using an adiabatic piston cylinder case. Then, simulation results of hook and claw type pump are compared with experimental data. Finally, simulation results of air and hydrogen are compared. The results show that the CFD simulation of hook and claw pumps using cut cell cartesian mesh could provide an efficient and effective approach for the optimization of the system.     

基于TRNSYS的茯砖茶生产工艺能源系统节能环保特性研究

茯砖茶发酵、干燥过程中，烘房内温湿度稳定性和能源系统低能耗是保证茯砖茶品质与成本的重要因素。本文采用TRNSYS仿真与实验研究相结合的方法，对咸阳某茯砖茶厂实际使用的空气源热泵系统进行建模，通过研究各季节典型代表月烘房温湿度的波动情况，确定该空气源热泵系统在全年的运行状态是否满足工艺要求，在此基础上，对比了该系统在全年可运行季节代表月与该生产厂房早期使用的燃气锅炉系统的能耗仿真结果，对空气源热泵系统的节能与环保特性进行研究。结果表明：由于夏季送风质量流量过大且室外空气含湿量较高，7月烘房温湿度不满足工艺要求。热泵系统在1、4、10月的总标煤消耗量的平均值是锅炉系统的44.42%，平均CO2、SO2、NOx排放量分别为锅炉系统的34.13%、44.1%、40.60%。在茯砖茶发酵、干燥的过程中，相比于燃气锅炉系统，空气源热泵系统具有更好的节能与环保特性。     

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。     

基于双离合变速器电控单元的Volcano CAN开发

Volcano 是集汽车电子网络设计、实现和测试于一体的完整工具链。它包括整车网络规划及优化设计工具VNA,网络节点(TCU等)实现工具VTP以及网络测试工具Tellus。本文着重介绍了基于TCU的VTP。     

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.     

Theoretical study of a thermoelectric-assisted vapor compression cycle for air-source heat pump applications

This paper proposes a thermoelectric-assisted vapor compression cycle (TVCC) for applications in air-source heat pump systems which could enhance the heating capacity of the system. Performances of TVCC are calculated and then compared with that of basic vapor compression cycle (BVCC). The simulation results show that when coefficients of performance (COPs) of the two cycles are almost equal, the TVCC under maximum COP condition of the thermoelectric modules still performs better than BVCC by 13.0% in heating capacity through selecting the appropriate intermediate temperature. In addition, the TVCC can also achieve an improvement of 16.4%–21.7% in both the heating COP and capacity when compared with the BVCC with an assistant electric heater that is provided with the equivalent power input of thermoelectric heat exchanger. Thus, the TVCC could be beneficial to the applications in small heat pumps if there is always need for auxiliary electric heat.     

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。     

燃气热水器在无回水管水路中实现零冷水的方案研究

本文分析了燃气热水器行业针对无回水管水路系统实现零冷水功能的现有技术方案,指出现有技术方案存在的一些缺陷,并结合试验研究,对无回水管水路系统提出了一套全新的实现零冷水功能的技术解决方案。