Vision-based tracking with projective mapping for parameter identification of remotely operated vehicles

Parameter identification of a remotely operated vehicle (ROV) is often based on the dynamic responses collected by its onboard sensors. However, for commercial ROVs, the required data for identification may not be available due to the absence of suitable sensors or limitations in accessing onboard sensor data. Therefore, this study proposes a vision-based tracking system to measure the dynamic response of an ROV. The tracking system is independent of the ROV, and is able to localize an ROV to a high degree of precision by means of projective mapping. The validity of the proposed tracking system is demonstrated through identification of a commercial ROV. A simplified nonlinear ROV dynamic model with six degrees of freedom (DOF) is used for identification. Uncoupled motions, including surge, sway, and yaw, are obtained from the ROV dynamic model, and the corresponding experiments are carried out for each DOF. Hydrodynamic parameters are then estimated with numerical optimizations by comparing the measured ROV responses with the output of the mathematical model. The experimental results show that the vision-based tracking system can accurately measure the transient and steady-state responses of an ROV. Additionally, the simulations of the ROV dynamic model, with the optimal parameter estimates, give results in agreement with the measured data.     

System identification for nonlinear maneuvering of large tankers using artificial neural network

This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn.     

一些海洋动植物区系的真菌分离和抑菌活性初步研究

从南海海域的鱼类、贝类、海藻以及海泥样品中分离出199株海洋真菌,分属于青霉属、曲霉属和酵母属等9个属,其中以青霉属和曲霉属真菌居多,分别占28.6%和26.8%。以大肠杆菌和金黄色葡萄球菌作为指示菌,筛选出抑菌活性菌株7株,其中2株既抗大肠杆菌又抗金黄色葡萄球菌,4株抗金黄色葡萄球菌,1株抗大肠杆菌。     

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西北贫困地区乡村类型识别与振兴途径研究

乡村振兴战略是新时期我国推进城乡融合发展的重要举措。贫困地区既是脱贫攻坚的主战场,又是乡村振兴的重点区域。如何科学识别村落类型,分类推进典型区域乡村振兴发展,对于破解农村发展不平衡不充分问题具有重要的现实意义。本文解析了乡村地域系统发展理论,阐释了城乡融合发展理论,剖析了村域空间结构格局演变和城乡要素结构功能优化重组;构建了西北干旱贫困地区乡村振兴村落类型识别体系,从乡村主体、产业发展、人居环境、资源禀赋4个子系统进行逐级分类判断,识别乡村主导类型,结合专家征询分析与村落特征,将乡村振兴村落类型识别为集聚提升类、三产融合类、城郊融合类、特色保护类、搬迁撤并类,结合不同乡村振兴模式提出了相应地发展路径,以期为乡村振兴提供理论依据与决策参考。     

一例异育银鲫(Carassius auratus gibelio)暴发性出血病病原分析

2016年6月,江苏某异育银鲫(Carassius auratus gibelio)养殖场暴发一种传染性急性出血病,造成养殖银鲫大量死亡。为分析此次疾病病因及流行规律,本研究从发病养殖场采集患出血病的异育银鲫,从细菌、病毒及寄生虫三个方面对病原进行了分析。采用病原菌分离、组织病理学观察、超薄切片电镜观察、病毒核酸分析、回感实验等对病原进行鉴定。结果显示从发病鲫鱼体内分离到病毒一株,未发现寄生虫及细菌感染。经测序及序列分析,该病毒为鲤疱疹病毒Ⅱ型(Cyprinid herpesvirus2,CyHV-2)病毒,组织病理学观察结果显示患病鱼的鳃和肾脏有明显病变,电镜下可观察到病鱼脾脏组织有带囊膜的球形病毒,囊膜直径约为170—200nm,病毒衣壳直径约为110—120nm,核心直径约为60nm,用组织匀浆感染鲫鱼囊胚细胞系(CGB)可稳定地观察到典型的细胞病变,用患病鱼组织匀浆液人工感染异育银鲫的死亡率高达100%,荧光定量PCR检测到该病毒可感染多器官,其中以脾脏中病毒含量最高,其次是脑,肝脏中最少。本研究可为CyHV-2的诊断防控及疫苗研制提供资料。     

经济海藻病害及防控技术的研究进展

经济海藻的病害一直是海藻人工栽培的一大障碍,往往造成严重的经济损失。目前对藻类病害的研究大多集中于病原鉴定,而病原的多样性和复杂的海区微环境增加了病原分离和鉴定的难度。本文综述了经济海藻病害发生的原因、病原的多样性和致病机制、病原的鉴定、检测和防治方法,并对未来我国海藻病害研究方向进行了展望,以期对藻类病害的防控提供参考。     

基于GF-3的不同极化方式SAR图像海面油膜识别的比较 ——以"桑吉"轮事故为例

基于我国自主研发的首颗分辨率达到1m的C频段多极化合成孔径雷达高分三号卫星(GF-3),通过典型溢油案例定性和定量地比较了双极化与单极化图像预处理性能,分析了不同的极化组合对海面油膜识别结果的影响。得出结论:同向极化与异向极化成像差异明显,VV极化明显比VH极化回波强度高,并且油膜识别度较高;相反,双极化VHVV方式与单极化VH方式图像效果区别不大,双极化方式并不能绝对性地提高地物识别度。     

柳珊瑚Leptogorgia rigida中甾体类成分研究

首次对南海柳珊瑚Leptogorgia rigida中甾体类次生代谢产物进行研究。采用硅胶柱柱层析、十八烷基硅烷键合硅胶填料、Sephadex LH-20、高效液相色谱(HPLC)及薄层色谱(TLC)等多种方法进行分离纯化, 通过核磁共振波谱(NMR)、质谱(MS)等现代波谱分析及物理常数对照等方法进行结构鉴定。从南海柳珊瑚Leptogorgia rigida次生代谢产物中分离得到8个甾类化合物, 结构依次为: 3β, 6β, 11-trihydroxy-9, 11-seco-5α-chloest-7-en-9-one (1); 3β, 6α, 11- trihydroxy-9, 11-seco-5α-chloest-7-en-9-one (2); 3β, 6α, 11-trihydroxy-5α-chloest-7, 22(E)-dien-9-one (3); pregnan-4-en-3, 20-dione (4); 5α-pregnan-3, 20-dione (5); 3β-hydroxy-pregnan-5-en-20-dion (6); 3β-hydroxy-5α-pregnan-20-one (7); 和3β, 6α, 11-trihydroxy-9, 11-seco-5α-gorgosterol (8)。所有化合物均首次从Leptogorgia属中得到。