Self-Adaptive Control System for Additive Manufacturing Using Double Electrode Micro Plasma Arc Welding

Wire arc additive manufacturing(WAAM)has been investigated to deposit large-scale metal parts due to its high deposition efficiency and low material cost.However,in the process of automatically manufacturing the high-quality metal parts by WAAM,several problems about the heat build-up,the deposit-path optimization,and the stability of the process parameters need to be well addressed.To overcome these issues,a new WAAM method based on the double electrode micro plasma arc welding(DE-MPAW)was designed.The circuit principles of different metal-transfer models in the DE-MPAW deposition process were analyzed theoretically.The effects between the parameters,wire feed rate and torch stand-off distance,in the process of WAAM were investigated experimentally.In addition,a real-time DE-MPAW control system was developed to optimize and stabilize the deposition process by self-adaptively changing the wire feed rate and torch stand-off distance.Finally,a series of tests were performed to evaluate the con-trol system's performance.The results show that the capability against interferences in the process of WAAM has been enhanced by this self-adaptive adjustment system.Further,the deposition paths about the metal part's layer heights in WAAM are simplified.Finally,the appearance of the WAAM-deposited metal layers is also improved with the use of the control system.     

变压吸附制氮(PSA)设备在炼油生产中的应用

针对某炼油装置氮气供需矛盾突出的问题,提出采用变压吸附(PSA)制氮设备,利用空压站净化风生产氮气方案,(1)降低空压站多余风量放空,减少浪费;(2)正常生产情况下,利用PSA设备生产氮气,对供氮管网压力调节。针对上述方案,结合空压站供风现状进行分析,得出结论:利用PSA制氮设备满足炼油装置高峰用氮是可行的,并提出了具体措施和运行过程中应注意的问题。     

超临界机组发生FAC的因素及相互关系分析

为了减轻因流动加速腐蚀(FAC)引起的锅炉结垢加速、汽水系统管道厚度减小甚至爆裂现象，对超临界机组发生流动加速腐蚀的机理及其主要影响因素进行了研究，并讨论了管壁内表面粗糙度、蒸汽含汽率、pH值、溶氧量对FAC的影响，以及温度与pH值、温度与流速、pH值与溶解氧量、溶解氧量与氢电导率等影响因素之间的相互作用关系，最后结合实际电厂的运行数据验证了分析结果。研究表明：减小工质流速、管壁粗糙度和氢电导率，增大给水的pH值和溶解氧含量可以使FAC的腐蚀速率减小，超临界加氧处理时pH值应在8.9～9.2之间，溶解氧量范围为45～100μg/L,氢电导率的期望值在0.1μS/cm以下。由于各影响因素之间的作用十分复杂，本文只给出了大致范围和趋势，并未给出准确数据。     

火电机组一次调频性能实时监测系统

为了实时监测和评价火电机组一次调频性能，开发了火电机组一次调频性能实测监测系统。该系统具有一次调频参数异动感知、一次调频性能量化预测及自适应校正等关键功能。某机组的实际应用表明，该系统能够实现火电机组一次调频关键参数的实时监测和调频性能的准确预测。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

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%。     

水-油连续螺旋折流板换热器热力性能研究

利用计算流体力学(Computational Fluid Dynamic, CFD)方法，针对连续螺旋折流板换热器建立物理模型和数学模型，在管侧介质为水和壳侧介质为原油条件下，研究不同原油流量及螺旋角对螺旋折流板换热器内部流场、换热性能及阻力性能的影响，并拟合了水油换热时螺旋折流板换热器的Nu、f与Re的关联式。结果表明：22°螺旋角的螺旋折流板换热器与其它较小螺旋角换热器对比，壳侧压降和换热系数逐渐减小，综合换热性能最佳。通过对壳侧原油为层流状态下的阻力系数和对流换热系数关系式进行拟合，更好地指导水-油连续螺旋折流板换热器的热力设计。     

离线式带钢双面翻转检查站方案设计及实践

针对某酸轧机组检查站穿带困难、检查效率低、产生废品多、故障率高的问题,开发了一种离线式带钢双面检查站。该检查站新增了带钢翻转装置,将在入口分切剪处完成定尺剪切的带钢试样由两侧压板夹紧,中间可升降皮带机落下,翻转装置将带钢试样张紧,并翻转90°或者180°,实现带钢的双面检查。皮带机两侧布置有可升降、可横移的活动操作平台,检测人员站在平台上对带钢进行打磨检查,可同时对带钢上下表面进行检测。由于缩短了带钢的传输距离,有效减少了其他擦划伤的影响,提高了带钢表面质量检测准确性。     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.