Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Currently,the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly.The primary advantage of these systems is that they do not need human participation in target recognition processes.This paper uses the particle swarm optimization(PSO)algorithm to select the optimal features in the micro-Doppler signature of sonar targets.The microDoppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers.Since different targets'geometric and physical properties are not the same,their micro-Doppler signature is different.This Inconsistency can be considered a practical issue(especially in the frequency domain)for sonar target recognition.Despite using 128-point fast Fourier transform(FFT)for the feature extraction step,not all extracted features contain helpful information.As a result,PSO selects the most optimum and valuable features.To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition,the simplest and most popular machine learning algorithm,k-nearest neighbor(k-NN),is used,which is called k-PSO in this paper because of the use of PSO for feature selection.The parameters measured are the correct recognition rate,reliability rate,and processing time.The simulation results show that k-PSO achieved a 100%correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-tonoise ratio(SNR)angle of 40°.Also,for the experimental dataset obtained from the cavitation tunnel,the correct recognition rate is 98.26%,and the reliability rate is 99.69%at 18.46s.Therefore,the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.

AN ALGORITHM FOR AUTOMATIC CARTOGRAPHIC SOUNDING SELECTION

Soundings on nautical charts provide information about the shape of ocean bottom between chart depth curves. A single chart may have thousands of soundings posted on it. This paper describes a new algorithm for selecting soundings automatically. At first,the problem encountered in automatic cartographic sounding selection is presented. Then the authors give the principle of the new algorithm,and the implementation is illuminated in detail. At last the experiments prove the rationality and efficiency of the new algorithm.

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

Visual attention and clustering-based automatic selection of landmarks using single camera

An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.

智能遮阳百叶眩光预测模型控制变量有效性分析

基于预测模型的智能遮阳百叶调控系统具有降低建筑室内眩光性能的能力,其智能性取决于调控方法。而调控方法的设计取决于对室内采光性能相关影响信息的思考。不同影响信息是调控反馈的依据,也是智能预测模型建构的基础。本研究利用机器学习技术,对不同百叶控制变量展开眩光关联性分析,以及不同控制变量在眩光影响上的交互效应。利用XGBoost构建预测模型与展开特征选择,同时采用SHAP可解释方法进一步解析变量与眩光关联大小。研究结果表明,室内使用者所在区域与遮阳百叶之间距离、遮阳形变与眩光发生最为紧密,且互动性影响明显,同时太阳高度角也是遮阳百叶重要的响应信息。结论可为智能遮阳百叶调控系统设计和预测模型建构提供参考依据。

水深点与等深线协同综合的强化学习方法

针对当前海图中水深点与等深线两要素的自动综合过程相对独立,二者相互影响考虑不够充分,导致结果不够理想的问题,提出一种水深点与等深线协同综合的强化学习方法。首先,获取用于水深点与等深线协同综合的训练样本;然后,构建并训练强化学习模型,挖掘水深点与等深线在综合过程中的相互影响关系;最后,利用训练后的模型,动态自适应调整水深点与等深线的综合策略。试验结果表明,在航海安全保证的可靠性、制图综合结果的图理性、海底地貌表达的准确性及海图要素分布的美观性等方面,强化学习法的性能均要优于当前常见的简单综合法、冲突避免法及协调水深法,更适用于处理水深点与等深线的协同综合问题。

油茶果壳籽实时分选系统设计与试验

为了实现油茶果脱壳后果壳与茶籽的高效分选,进行了油茶果壳籽分选装置上料模块、视觉采集模块、执行模块和控制系统的设计。其中,整体装置采用一种阵列式凹槽传送机构,避免执行喷吹时产生对茶籽的带出干扰。利用有效的图像预处理方法获取壳籽目标,并提取目标图像的颜色及纹理信息共18个;考虑到分选的高效实时性,进一步对特征参数实施Spearman相关性分析,将特征减少至6个,建立随机森林分类模型,并与K最邻近分类算法(KNN)、支持向量机(SVM)及偏最小二乘法判别分析(PLS-DA)模型进行对比,发现随机森林模型的分类效果最佳。试验中,分选系统识别准确率达到95.5%,平均检测时间为41.9ms,分选速率达20个/s,表明系统实现了油茶果的壳籽高效率实时分选。

自动跟踪定位射流灭火系统电气设计探讨

结合相关规范要求,分析自动跟踪定位射流灭火系统的探测、控制、联动等应用特点,指出在电气设计中容易忽视的部分,同时对系统线缆选型的安全性进行探讨,并对实际系统的技术应用提出总结和建议。

环境复杂度量化评价下的车位选择方法

为提高自动泊车成功率,建立车位环境复杂度量化评价模型.以车位类型、车位线属性等7个指标建立车位环境复杂度评价指标体系,并提出相应的量化表征方法.针对主客观组合赋权法无法正确计算单个样本临界指标权重的问题,在层次分析法基础上,提出基于平方和归一化的权重二次分配方法.运用模糊综合评价求解车位环境复杂度,通过设计仿真测试来验证所提模型.结果表明:与层次分析法、熵权法相比,所提赋权方法能够正确对临界决定性指标赋予较大权重,避免无效权重赋予;所提模型能够正确反映车位环境复杂度变化,对泊车成功率有较大提升,为泊车位的自动选择提供一种可行的新路径.

催化裂化分馏系统的自控设计及仪表选型研究

按照“化工装置自控工程设计规定”根据工艺提出的检测及控制要求,对催化裂化分馏装置的分馏塔、汽提塔、油气分离器、原料油缓冲罐和回炼油管的液位、温度、压力等进行控制和检测,并对流程做了简单的介绍。根据催化裂化分馏装置的特点选择了合适的仪表和调节阀。

石油催化裂化分馏系统的自控化设计及仪表选型分析

按照“化工装置自控工程设计规定”,根据工艺提出的检测及控制要求,对催化裂化分馏装置的分馏塔、反应-再生系统、回炼油罐、汽提塔的液位、温度、压力等进行控制和检测,并对流程做了简单的介绍。根据催化裂化分馏装置的特点选择了合适的仪表和DCS系统。

基于污水厂扩容的自动化控制系统的应用

简要介绍了自动化控制系统在污水厂扩容项目中的设计和应用。以湛江市霞山污水厂扩容项目为例,详细分析和介绍了污水厂自控系统设计思路、系统架构搭建、PLC站选型配置、工艺设备控制方式等,以期为同类型的污水厂扩建中自动化控制系统建设提供参考。

浅谈硫磺回收装置设计过程中针对硫化氢介质的防范措施

石油化工生产中,往往伴随有毒有害介质如硫化氢的生成。本文针对硫化氢各种危害情况,简单阐述了在硫磺回收装置设计过程中针对其泄漏所作的部分防范措施,详细描述了密闭排放等方式。供化工企业或行业同仁参考、讨论。

高速铁路引入枢纽信号系统优化设计

高速铁路引入枢纽给信号系统设计带来挑战,结合京沈客专引入北京枢纽,从枢纽内列控等级选择、场联分界处自动触发、提高枢纽整体运行效率等方面提出优化方案,以期对其他类似工程建设起到借鉴、参考作用。

HARQ Scheme for MIMO Systems with Antenna Selection

A new type hybrid automatic repeat request(HARQ) scheme based on orthogonal space-time block code(OSTBC) is proposed and evaluated in multiple-input multiple-output(MIMO) systems.The transmit antennas are selected based on the feedback from the receiver.The modulated symbols are encoded by spacetime block code(STBC) and obtained by space time code blocks which are suitable for the number of selected antennas.The total packet error rate(PER) is given by defining the decoding function and the best value of the maximum number of HARQ retransmissions is discussed.Theory and simulation results illustrate that:this new HARQ scheme with antenna selection method has lower PER and the result is also satisfying.

2022中国国际纺织机械展览会暨ITMA亚洲展览会染色机械述评

综述了2022中国国际纺织机械展览会暨ITMA亚洲展览会染色机械相关情况。对本届展会展出的多种染色机械、印染厂化验室设备和染化料自动配送系统进行重点介绍和详细分析。指出本次展会染色设备紧密贴合绿色、科技、高效的发展主题,正向数字化、智能化方向发展。

一种新型AGV驱动单元设计及应用分析

为解决AGV在行走过程中驱动轮打滑问题,进一步提升AGV行走稳定性,降低AGV的振动、噪声,在现有AGV驱动单元的研究现状基础上,运用模块化的思想设计了一种新型独立减震的AGV驱动单元,详细阐述了其结构组成、工作原理、主要特点,分析了AGV在行走过程中的阻力及其计算方法,同时给出了AGV驱动单元选型计算的范例。此外,还对新型AGV驱动单元的典型应用进行了分析,给出了驱动单元与载荷及AGV自重的匹配条件。所设计的新型AGV驱动单元不仅能解决打滑问题,结构也更加紧凑,为其推广应用提供参考。

采用样本自动选择的建筑物遥感场景分类方法

目前遥感图像建筑物场景分类方法多采用人工标注样本,标注过程需要大量时间。针对该问题,提出了一种采用样本自动选择的高分遥感建筑物场景分类方法。首先,建立光谱特征、几何特征和深度特征的多维高分遥感图像影像对象特征空间;其次,采用决策树初步提取建筑物,构建建筑物场景密度直方图;然后,采用自然间断法对建筑物密度分级,并采用比例法分别在每类场景中提取部分场景图像作为训练样本;最后,采用ResNet50网络对建筑物场景分类。以辽宁省沈阳市浑南区为研究区域和Google Earth遥感图像为实验数据,实验结果表明本文方法能够实现非监督场景分类,总体分类精度和Kappa系数分别为0.89和0.82,较原有样本选择方法分类精度提高了3%和8%。

铁路BIM选线设计方法研究

BIM技术应用是工程建设领域近年研究热点,依托三维可视化、多专业协同化优势已广泛在建筑、机电设备等领域中应用。我国铁路行业BIM技术发展较晚,但当前已实现桥、隧、站、线路等多个专业领域内的BIM研究设计,通过应用中铁二院自主研发三维空间自动选线系统自动生成初步方案群、BIMRDS系统平纵三维一体化优化调整等策略探索了线路BIM技术的实施技术路线,并通过案例进行了验证,结果表明,路线可行,成果可显著提高设计质量及效率。

特征点匹配和互信息融合下连续形变图像配准

图像配准中易受到外界干扰发生变形或者部分特征缺失,为此提出融合特征点匹配和互信息的连续形变图像配准方法。提取连续形变图像特征,对提取到的特征实施匹配处理,结合互信息算法统计两图像之间的相似程度,得出最佳配准指标,即像素灰度值为最大值,结合特征点匹配结果以及互信息算法生成图像配准刚性指标,实现连续形变图像配准。实验结果表明,所提方法配准后图像信息完整,配准效果较好,且匹配耗时较短,说明其能够降低图像配准中干扰因素的影响,有效提升图像配准质量。