机载激光雷达数据的三维深度学习树种分类

针对传统基于激光雷达(Light Detection And Ranging,LiDAR)数据的树种分类方法难以直接且全面地利用点云的三维结构信息的问题,提出一种基于三维深度学习的机载LiDAR数据的树种分类方法.该方法直接从三维数据中抽象出高维特征,而无须将点云转化为体素或二维图像.以塞罕坝国家森林公园内白桦和落叶松...     

导弹突防后弹道机动调整策略强化学习

针对弹道导弹中段突防后飞行弹道与标准弹道产生较大偏离的弹道机动调整问题,建立了机动调整时机策略最优化模型。设计了机动调整逆序Q学习算法,采用Tile coding逼近器编码状态特征空间,并对其进行线性逼近。构建了Q学习算法与蒙特卡罗方法相结合的逆序更新策略机制,以对导弹机动调整最优时机进行训练。仿真测试分析结果表明,在给定场景参数下,通过10 000代强化学习算法训练得到的策略能够可靠地使用最少机动次数控制导弹突防后飞行弹道的调整决策,验证了方法的有效性。     

论法治进程中的正当程序

法治的实现有赖于法治要素的具备,而正当程序有助于法治要素的满足。法治社会应具有的正当程序理念是:正当程序是对人权的尊重和保护,是实现正义的重要途径,程序正义优先于实体正义。     

秘密监听原则探析

秘密监听对公民宪法所保障的权利和自由产生了威胁,但是对侦查日益智能化的犯罪又是不可或缺的证据收集方式。从适应刑事庭审方式对证据要求的角度对秘密监听加以规范,使其以最小化的侵犯公民权利、自由的方式,达到证据收集效率的最优化,使以秘密监听收集方式获得的证据不致因违法而被排除在法庭之外,就必须从理论上解决秘密监听的法理依据及其密切相关的强迫自证其罪与拒绝证言权等问题,并使其能够最终成为定案的依据。     

非平衡等离子体及其军事应用研究进展

高气压非平衡等离子体是当今世界经济、军事强国竞相研究的焦点,涉及工业、军事的高能物质(活性粒子)加工及其辐射的等离子体源及反应器(室)。目前,高气压非平衡等离子体及其源的研究局限于弱(电场)电离放电范畴,存在等离子体浓度低、能耗甚高和体积庞大等问题。为此,研究外加非均匀强电场、空间电荷形成的本征电场对离子的作用力及其运动规律,以便解决形成高浓度等离子体的方法及离子从强电场束缚中引出去的问题,为研制强电离放电非平衡等离子体源提供了理论基础及加工方法,此源外输的等离子体束的浓度有望达到1014/cm3。等离子体源及反应器可以做到微型化,每立方厘米有效放电体积处理气量高达15m3/h。它的体积、能耗、一次造价、运行成本等也将成万倍地降低,解决了等离子体工程化的现存问题;也能解决困扰世界各军事强国的飞行器等离子体隐身、减阻及天线的应用理论与方法问题。     

具有不确定信息的作战方案优选的群决策方法

根据群决策的特点,按照集议决策的习惯程序,提出了一种充分发挥集议决策优势的群决策方法。并基于区间分析对潜艇布雷方案优选中的不确定性进行处理,最后利用了区间数多属性决策的TOPSIS法行为集结的最后结果（决策矩阵）进行数学集结,较好地做到了决策在定性和定量上的综合集成。     

美国非法证据排除规则与德国证据运用禁止规则之比较

非法获得的证据能否作为定案的依据 ,历来为各国刑事证据理论所关注。美国和德国作为英美法系和大陆法系的典型代表 ,分别确立了非法证据排除规则和证据运用禁止规则。研究了两国排除规则的内涵、发展历史、适用范围及存在差异     

背景音乐对人类学习效率影响的脑与行为研究进展

背景青乐是与主体的意识或行为无直接关系,通过非青乐鉴赏环境这一媒介间接作用于主体意识与行为的音乐。近些年来将背景音乐引入教育领域来探讨其对学习效率的影响是研究的新课题。从背景音乐对学习效率影响的相关理论、背景音乐对学习效率影响的实验研究以及背景音乐影响的研究趋势等方面进行了综述。     

BSC信道下线性分组码的差错概率下界分析

针对BSC信道,提出了一种线性分组码的最大似然译码差错概率下界的计算方法.根据最大似然译码算法原理,首先将译码差错概率转化为差错事件的联合概率,基于改进的Dawson-Sankoff界的优化准则,推导出BSC信道下线性分组码差错冗余事件的判决准则,最后得到差错概率下界的计算表达式.该下界只依赖于码字的Hamming重量...     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.