Improved lazy theta algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

Lazy loading algorithm for traffic assignment of road networks under fixed charge condition

The measures of path charge are important considerations in traffic assignment of road networks. Factors, such as travel time, fixed charge and traffic congestion which affect road users＇ choices of trip paths, are analyzed. Travelers usually decide their trip paths based on their personal habits, preferences and the information at hand. By considering both deterministic and stochastic factors which affect the value of time （VOT） during the process of path choosing, a variational inequality model is proposed to describe the problem of traffic assignment. A lazy loading algorithm for traffic assignment is designed to solve the proposed model, and the calculation steps are given. Numerical experiment results show that compared with the all-or-nothing assignment, the proposed model and the algorithm can provide more optimal traffic assignments for road networks. The results of this study can be used to optimize traffic planning and management.

陷阱标记联合懒蚂蚁的自适应粒子群优化算法

为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。

密集城市群中基于智能反射面的传输方案

由于低频段资源紧缺,未来移动通信网络将逐渐转移至更高频段,电磁波的自由空间传播损耗、绕射损耗以及穿透损耗较大,容易产生覆盖盲区以及弱覆盖区域。智能反射面(RIS)通过重新配置信号的传播环境以提高网络性能并扩大网络覆盖范围,成为解决密集城市群覆盖问题的新热点。基于有RIS参与的下一代移动通信网络密集城市群布网时的多跳通信模式,对多跳RIS传输方案与提出的一种解码转发(DF)中继辅助多跳RIS传输方案(DF-RIS)进行全面比对。首先,对两种传输方案的传输损耗、能效以及系统容量进行了理论推导及分析。其次,在密集城市群三维环境模型中进行仿真,为改善复杂环境搜索效率降低以及计算量增大的问题提出了一种改进Lazy Theta*算法,并对改进算法与常见路径搜索算法的搜索时间以及搜索路径长度进行仿真对比。使用改进算法选取相同仿真环境下两种传输方案分别所需节点,节点选取中考虑了DF中继位于多跳DF-RIS传输方案中的不同位置。最后在确定节点后对两种传输方案的传输损耗、能效以及系统容量进行仿真分析比较。仿真结果不但验证了改进Lazy Theta*算法的有效性,也说明了RIS反射元件数与两种传输方案性能之间的关系。表明两种传输方案各有其适用性,可根据对性能的实际需求选择合适的传输方案。

基于区块链的智能电网隐私保护数据聚合方案

为解决电力用户用电数据隐私泄露的问题,提出一种基于区块链的智能电网隐私保护数据聚合方案。每个智能电表分别使用Okamoto-Uchiyama加密算法和双线性聚合签名算法对用电数据进行处理,将其上传至区块链主节点。区块链主节点负责验证签名并聚合数据后将其存入区块链。电力公司从链上获取聚合数据。由于区块链主节点可能为了节省计算资源而不验证用户签名,发起懒惰攻击,引入可选性检验阶段,避免这种攻击。安全分析和性能评估表明,所提方案具有较好的安全性,能够以较低计算代价取得更优整体性能。

Parallel Expectation-Maximization Algorithm for Large Databases

A new parallel expectation-maximization （EM） algorithm is proposed for large databases. The purpose of the algorithm is to accelerate the operation of the EM algorithm. As a well-known algorithm for estimation in generic statistical problems, the EM algorithm has been widely used in many domains. But it often requires significant computational resources. So it is needed to develop more elaborate methods to adapt the databases to a large number of records or large dimensionality. The parallel EM algorithm is based on partial Esteps which has the standard convergence guarantee of EM. The algorithm utilizes fully the advantage of parallel computation. It was confirmed that the algorithm obtains about 2.6 speedups in contrast with the standard EM algorithm through its application to large databases. The running time will decrease near linearly when the number of processors increasing.

基于遗传算法的缓波型立管多目标集成优化

缓波型立管由于设计参数较多且优化目标之间相互影响,设计结果具有很大的不确定性。随着代理模型和智能优化算法的发展,针对缓波型立管的优化可以提出更好的解决方案。以提高力学性能和经济效益为优化目标,采用基于Kriging插值模型和NSGA-II算法的多目标优化策略,对考虑顶部浮体影响的深水缓波型立管进行动力响应分析,并开展线型—截面双目标优化集成设计和线型—浮筒三目标优化集成设计。将处于不同几何尺度的设计变量进行集成,旨在各目标存在相互竞争的情况下,与截面、浮筒设计形成有效互动以提高线型设计的总体性能。结果表明,Pareto最优解集可提供多个选择方案,以满足工程实际需要。将所选最优方案与初始设计进行对比,并以疲劳性能和成本估算作为优化的校核指标,取得了理想的优化效果。

基于拓扑势的网络毁伤最大算法

针对攻击代价相等时的有限资源网络毁伤问题,给出了网络毁伤最大化的定义。为了改进近似求解算法求解毁伤最大化问题时复杂度较高的缺陷,提出了基于拓扑势和CELF(cost-effective lazy-forward)的TPCELF(algorithm based on topology potential and CELF)算法。利用无标度网络和实测网络进行实验,结果表明,TPCELF算法在计算速度上有较大的提升,网络平均毁伤效果接近于近似求解算法;且优于采用常见重要性度量指标排序算法得到的平均毁伤效果。所提方法可从网络毁伤的角度为复杂网络关键节点挖掘提供参考。

面向物联网无人机通信的短突发CPM信号盲均衡算法

针对物联网无人机通信中短突发连续相位调制(continuous phase modulation, CPM)盲均衡算法复杂度高、收敛差等问题,提出了一种基于期望最大化-维特比(expectation maximization Viterbi, EMV)的盲均衡算法。首先,将Lazy维特比算法嵌入到期望最大化(expectation maximization, EM)算法的迭代过程中,得到一种低复杂度的盲均衡算法;然后,基于盲信道捕获(blind channel-acquisition, BCA)方法构建了一组初值集,克服了算法对初值的敏感性,同时提升了信道估计的收敛性能。理论分析和实验结果表明,所提的盲均衡算法能够有效兼顾收敛性和计算复杂度,相比传统方法,在性能损失很小的条件下可大幅度降低计算成本,并且具有良好的鲁棒性。

面向无人机数据采集的短突发CPM盲Turbo均衡

在无人机辅助的战场侦察传感器数据采集中,短突发连续相位调制(continuous phase modulation,CPM)具有重大的实用价值,但也面临着盲均衡复杂度高、性能差等问题。基于期望最大化(expectation-maximization,EM)-维特比类盲均衡算法,针对短突发CPM提出了一种盲Turbo均衡算法。采用盲信道捕获方法设计了一组初值方案,同时在EM算法中嵌入Lazy维特比算法,提高了信道估计收敛性并降低了复杂度;接着在编码CPM中匹配卷积码和交织器,优化系统参数,并利用Turbo均衡进一步改善误码率;针对短帧迭代过程中交织深度不够导致的正反馈问题,通过改进外信息交换方式提升Turbo迭代的收敛性。理论分析和仿真结果表明,相比传统方法,所提算法在显著降低计算复杂度的同时能够兼顾误码率性能。

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation functions, such as ReLU, logistic (sigmoid), and Tanh. Both methodologies produced optimal models with high-level accuracy rates. The first methodology involves a list of supervised machine learning algorithms, including KNN, SVM, Adaboost, Logistic Regression, Naive Bayes, and Decision Tree algorithms. They achieved accuracy rates of 86%, 89%, 89%, 81%, 79%, and 99%, respectively. The author clearly explained that Decision Tree algorithm is not suitable for the dataset at hand due to overfitting issues. Therefore, it was discarded as an optimal model to be used. However, the latter methodology (Neural Network) demonstrated the most stable and optimal accuracy, achieving over 87% accuracy while adapting well to real-life situations and requiring low computing power overall. A performance assessment and evaluation were carried out based on a confusion matrix report to demonstrate feasibility and performance. The author concluded that the performance of the model in real-life situations can advance not only the medical field of science but also mathematical concepts. Additionally, the advanced preprocessing approach behind the model can provide value to the Data Science community. The model can be further developed by employing various optimization techniques to handle even larger datasets related to heart failures. Furthermore, different neural network algorithms can be tested to explore alternative approaches and yield different results.

移动机器人平滑JPS路径规划与轨迹优化方法

针对目前路径规划方法存在的平滑性和效率问题,在JPS算法基础上提出了兼顾平滑性与搜索效率的路径规划方法,并利用多项式进行了轨迹优化。首先,提出2个优化目标对路径序列进行优化处理;然后,对JPS搜索规则进行改进,得到更多有价值的路径,并对每条路径进行平滑处理,再以一定规则进行选择;最后,使用多段高阶多项式对所得路径进行轨迹优化,研究时间分配问题,从而加快迭代效率。通过仿真实验和与其他算法的对比证明了本文方法的可行性和有效性。结果表明,在不同障碍物密度环境下,本文路径规划方法得到了平滑性良好的路径,相对平滑后处理JPS,长度减少了0.48%~1.80%,总转折角减少了16.93%~52.75%,利用余弦函数进行时间分配加快了轨迹优化的迭代效率,通过实验验证得到了良好的效果。

可扩展Hash方法的一种改进算法

文章介绍了可扩展Hash方法,并提出了改进的缓存算法,该算法可以避免因为伪键分布异常而出现频繁的桶分裂及Hash表更新操作,从而提高空间和时间效率,有效地利用服务器资源。

新型同步发电机励磁控制器的设计

针对传统的基于迭代学习控制算法的同步发电机励磁控制器存在初始控制信号由经验确定的问题,提出了一种基于即时学习型迭代学习控制算法的同步发电机励磁控制器的设计方案。该方案在迭代学习控制算法中引入即时学习算法,利用即时学习算法计算初始控制信号,有效减少了初始控制信号与理想控制信号之间的误差。仿真结果表明,该励磁控制器收敛速度快,具有更强的维持机端电压的能力。

基于即时学习算法非线性系统多模型自适应控制

针对可获得大量输入输出数据的非线性系统 ,提出一种改进的即时模型辨识方法 ,并与自校正的极点配置控制算法相结合 ,设计多模型自适应控制器 .所提出的建模方法和相应的多模型自适应控制器能较好地逼近非线性系统的动态特性 。

基于感知时间的交通分配模型及其算法研究

路径的感知时间主要受定量和随机两方面因素的影响．在实际的交通网络中，由于时间价值观的不同，道路使用者会根据习惯、偏好、信息而对出行时间、费用、道路拥挤等诸多影响因素做出不同的反应。本文通过综合考虑定量和随机两方面因素的影响效应，建立了基于感知时间的交通分配模型，设计了求解该模型的延迟加载算法。最后结合算例验证了算法的有效性．

基于生命期预测的分代式垃圾收集算法

针对嵌入式虚拟机中垃圾收集效率低的问题,提出一种新的分代式垃圾收集方法。通过对分配对象的生命期进行预测,将生命期长的对象直接分配到年长代,减少了从年轻代到年长代复制的数量,进而缩短垃圾回收的执行时间。在分代内收集方面,年轻代实现一种对象分配和垃圾收集同时进行的非暂停模式,年长代使用lazybuddy算法结合mark-sw eep算法的综合策略,实现空间的快速分配和回收,既避免了对象拷贝操作,又控制了内存碎片数量。实验结果表明,该算法能够使垃圾收集时间减少约23.9%,用例运行时间减少约17.2%,系统整体执行性能得到明显提升。

一种适合于大数据集处理的混合EM算法

EM算法的计算强度较大,且当数据集较大时,计算效率较低。为此,提出了基于部分E步的混合EM算法,降低了算法的计算强度,提高了算法对数据集大小的适应能力,并且保持了EM算法的收敛特性。最后通过将算法应用于大的数据集,验证了该算法能减少计算强度。

PCS网络位置管理方案性能分析

PCS(Personal Communication Service)网络中位置管理开销昂贵;为减小开销,研究人员提出了许多种方案。研究了基于LRA(Lazy Replication Algorithm)的位置管理方案,建立了分析模型,以相邻两次呼叫期间实现位置管理所花费的开销为指标,对IS-41和LRA两者的性能进行了比较。研究表明,对于高移动性或远离归属地的用户,LRA显著优于IS-41;另一方面,对于呼叫多发生于两个服务区间或低移动性的用户I,S-41优于LRA;从总体上看,LRA性能优于IS-41。

一种融入注意力和预测的特征选择SLAM算法

针对SLAM(simultaneous localization and mapping)在急转弯、快速运动场景中定位失败的问题,提出一种融入注意力和预测的特征选择即时定位与地图创建(SLAM)算法,选择随着相机的运动更有可能保持在视野中的特征点,舍去即将消失在视野中的特征点。首先利用logdet度量量化特征选择的可行性,然后计算特征点的信息矩阵,再从检测到的特征中通过贪婪算法选择k个特征(近似的)最大化logdet度量,最后结合ORBSLAM2的实际实验表明,该算法在复杂场景(如急转弯、快速运动)中可以确保定位的准确性。