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.     

Designing independent water transport channels to improve water flooding in ultra-thin nanoporous film cathodes for PEMFCs

Utilization of 3D nanostructured Pt cathodes could obviously improve performances of proton exchange membrane fuel cells (PEMFCs) owing to the reduced tortuosity and the bi-continuous nanoporous structure. However, these cathodes usually suffer from the flooding problem ascribed to the ionomer-free and nanoscale pores which are more susceptible to water condensation. In this paper, ultra-thin nanoporous metal films (100 nm) were utilized to construct PEMFC cathodes and independent transport channels were designed separately for water and gas aiming at the flooding problem. Nanoporous gold (NPG) film was used as the model support for loading Pt nanoparticles owing to its controllable and stable structure. After optimizing the polytetrafluoroethylene (PTFE) content and carbon loading in the gas diffusion layer (GDL), plasma treatment under O₂ atmosphere was used to pattern the GDL with independent water transport channels. The obtained liquid permeation coefficients and oxygen gains demonstrated the obviously improved water and O₂ transport. By using a home-made optimized GDL and a nanoporous film cathode with pore size ～60 nm, the flooding problem could be facilely solved. With a Pt loading of ～16 μg cm^?2, this 3D nanostructured cathode exhibits a PEMFC performance of ～957 mW cm^?2 at 80 °C. The Pt power efficiency is about 4 times higher than that of the commercial Pt/C cathode (50 μg cm^?2, 756 mW cm^?2). Obviously, this study provides a simple but effective methodology to solve the water flooding problem in the ultra-thin nanoporous film cathodes which is applicable for other types of 3D nanostructured PEMFC cathodes.     

基于概要数据结构的全网络持续流检测方法

持续流是隐蔽的网络攻击过程中显现的一种重要特征，它不产生大量流量且在较长周期内有规律地发生，给传统的检测方法带来极大挑战。针对网络攻击的隐蔽性、单监测点的重负荷和信息有限的问题，提出全网络持续流检测方法。首先，设计一种概要数据结构，并将其部署在每个监测点；其次，当网络流到达监测点时，提取流的概要信息并更新概要数据结构的一位；然后，在测量周期结束时，主监测点将来自其他监测点的概要信息进行综合；最后，提出流持续性的近似估计，通过一些简单计算为每个流构建一个位向量，利用概率统计方法估计流持续性，使用修正后的持续性估计检测持续流。通过真实的网络流量进行实验，结果表明，与长持续时间流检测算法（TLF）相比，所提方法的准确性提高了50%，误报率和漏报率分别降低了22%和20%，说明全网络持续流检测方法能够有效监测高速网络流量。     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

Study of the influence of notch radii and temperature on the probability of failure: A methodology to perform a combined assessment

The fracture assessment of notched components based on cracked components approaches leads to over‐conservative failure predictions. In the research literature, several approaches are proposed to overcome this problem using an apparent fracture toughness, . Nevertheless, most of these approaches are based on deterministic assumptions despite the large and variable scatter exhibited by for different notch radii (ρ) or temperatures (T). This paper proposes a methodology for deriving a probabilistic field including the effect of temperature on the failure of notched components. First, the theory of critical distances is applied to transform each apparent fracture toughness into the equivalent fracture toughness for ρ = 0. Then, the temperature is supposed to act as a scale effect in the Weibull cumulative distribution function of the equivalent fracture toughness, and the corresponding scale effect function is derived. Finally, the applicability of the proposed methodology is illustrated by an example using two ferritic‐pearlitic steels: S275JR and S355J2.     

油藏中后期减氧空气驱提高采收率机理研究

尕斯库勒油田E¹₃油藏已经进入高含水时期，储采失衡加剧，综合含水率达到了80%以上，原油产量下降，经济效益变差。为了提高油藏原油采出程度、改善油藏开发效果，调研了国内外减氧空气驱的机理研究以及现场试验的资料，建立了以油藏真实孔渗饱数据为基础的一维条形地层机理模型，并选取该油藏Y12-27井组进行了减氧空气驱可行性验证。研究表明:纵向顶部减氧空气驱驱油效果优于水驱和氮气驱；驱替压力对原油采出程度影响不大；注水转注气可以提高原油采出程度；对于减氧空气驱，由于低温氧化反应的作用，氧气浓度对原油采出程度有一定影响，但比较微弱，其中，氧气浓度为10%时，驱替结束采出程度最高；尕斯库勒油田E¹₃油藏属于注水开发“双高”油藏，适用于减氧空气驱；对于该油藏Y12-27井组，顶部减氧空气驱驱油效果好于水驱和氮气驱，建议氧气浓度超过10%时采取关井等措施。     

CO2驱沥青质沉积对致密储层的伤害机理——以鄂尔多斯盆地延长组长8储层为例

为了揭示CO2驱替过程中沥青质沉积对致密储层的伤害机理,文中基于岩心核磁共振T2谱测试原理,开展了CO2注入压力下的岩心驱替实验,研究了沥青质在岩心中的沉积特征,评价了沥青质沉积对储层的伤害程度。实验结果表明:沥青质沉积量和渗透率伤害率随着CO2注入压力的升高呈现先快速上升后趋于平缓的趋势;在CO2注入过程中,沥青质主要沉积在弛豫时间大于10 ms的大、中孔隙,导致大、中孔隙占比下降,微细、小孔隙占比增加,且随着CO2注入压力的升高,大、中孔隙占比下降幅度增大,微细、小孔隙占比上升幅度增大;此外,沥青质沉积会引起润湿性发生反转,随着沥青质沉积量的增加,润湿反转指数增大,岩心润湿性不断向强油湿方向转变。     

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods.     

三元复合驱采出水处理研究进展

随着三元复合驱采油技术在油田的广泛应用，三元复合驱采出水产量不断增加。三元复合驱采出水水质复杂，具有矿化度高、黏度大、含油乳化程度高、小油滴含量高、油水分离困难等特点，对油田生产和环境的影响日益严重。因此，开展三元复合驱采出水高效处理方法的研究成为一项重要的任务。本文分析了影响三元复合驱采出水油水分离特性的各种因素，介绍了目前国内应用于三元采出水处理的先进技术，如膜分离法、气浮选分离法、高级氧化法、微生物法等，阐述了这些处理技术的优势及存在的问题，重点介绍了气浮选分离法和微生物法在三元采出水处理中的应用情况，并对大庆油田三元复合驱采出水现场处理工艺进行了介绍，最后对今后的研究工作提出了一些展望。