基于Q-learning的不确定环境BDI Agent最优策略规划研究

BDI模型能够很好地解决在特定环境下的Agent的推理和决策问题,但在动态和不确定环境下缺少决策和学习的能力。强化学习解决了Agent在未知环境下的决策问题,却缺少BDI模型中的规则描述和逻辑推理。针对BDI在未知和动态环境下的策略规划问题,提出基于强化学习Q-learning算法来实现BDI Agent学习和规划的方法,并针对BDI的实现模型ASL的决策机制做出了改进,最后在ASL的仿真平台Jason上建立了迷宫的仿真,仿真实验表明,在加入Q-learning学习机制后的新的ASL系统中,Agent在不确定环境下依然可以完成任务。     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

一种军棋机器博弈的多棋子协同博弈方法

针对在军棋博弈不完全信息对弈中，面对棋子不同价值、不同位置、不同搭配所产生的不同棋力，传统的单子意图搜索算法，不能满足棋子之间的协同性与沟通性，同时也缺乏对敌方的引诱与欺骗等高级对抗能力。本文提出一种结合UCT搜索策略的高价值棋子博弈方法，实现高价值棋子协同博弈的策略。实战经验表明:高价值多棋子军棋协同博弈策略优于单棋子军棋博弈策略。     

重力坝坝基多斜面抗滑稳定模糊体系可靠度研究

以基岩具有多斜面、多滑移通道的某实际重力坝工程为例,基于刚体极限平衡法,采用模糊理论和几种典型的体系可靠度计算方法,研究其典型坝段的坝基深层抗滑稳定模糊体系可靠度,探讨模糊理论中隶属函数形式、隶属度以及隶属函数中关键参数的取值对可靠度计算结果的影响。研究结果表明,采取限制失效概率模糊界限比的方法可使计算结果更具有参考性,考虑模糊性的抗滑稳定可靠指标的下限较常规可靠度计算值偏小,这在重力坝坝基抗滑稳定分析中应引起重视。     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

Performance discrepancies on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample.

The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score points (depending on the individual comparison) in the hypothesized direction to be considered potentially unusual. However, because about 1 in 3 persons in the standardization sample displayed at least 1 such large discrepancy, it is concluded that these base rates should be viewed only as a starting point for interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

大庆长垣以东地区深层天然气输导特征及成藏

大庆长垣以东地区深层天然气输导通道主要由断裂、不整合面和砂体构成。该区砂体横向分布不稳定，物性差，不是天然气侧向长距离运移的输导通道，但可以起到衔接输导作用。输导通道时该区天然气成藏与分布起到了重要控制作用，主要表现在断裂将天然气输导致上覆不同储集层中聚集成藏。不整合面和砂体将天然气输导到四周古隆起上聚集成藏。沿断裂输导的天然气形成以断层为主的构造气藏。沿不整合面和砂体输导的天然气形成以地层及岩性为主的气藏。沿断裂输导运移聚集的天然气在剖面上呈串多层分布，沿不整合面和砂体输导运移聚集的天然气在平面上呈环状分布。     

深水井控中节流管线摩阻分析及压井方法

在海洋深水钻井中，防喷器组一般安装在海底，当发生井涌进行压井作业时．由于节流管线较长、内径较小，钻井液在节流管线内存在高摩阻损失，作用在井底的压力往往大于地层的破裂压力．从而引起压井过程的复杂化，甚至造成压井作业的失败。介绍了一种新的压井方法附加流量法，在返出的节流管线流体中混入低密度流体，降低节流管线内流体的粘度和密度，从而实现安全压井；详细介绍了压井方法和步骤。数据分析表明，通过对压井参数进行优化选择能够降低节流管线中的压耗。     

催化裂解多产丙烯新技术

介绍了国内外利用蜡油或常压渣油等重质原料的增产丙烯技术CPP,ARGG FDFCC和TSRMP;还介绍了利用富含烯烃的混合碳四和催化裂化汽油等轻质原料的增产丙烯技术OCP,Propylur,Superflex,OCT,Meta-4和BASF公司的丁烯歧化工艺技术.