Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network

With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering.     

Optimized support vector regression algorithm-based modeling of ship dynamics

This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types’ ships and also validate the performance of the proposed parameter estimation method.     

FAM人工神经网络模型在震后趋势判断中的应用

介绍了FAM模糊人工神经网络的原理、学习方法与规则,提出了用于地震震后序列3种震型的判断标准及现场预报规则,建立了基于FAM人工神经网络模型的地震现场预报手段与准则.     

Advances of geo-spatial intelligence at LIESMARS

ABSTRACT

The enhancement of computing power, the maturity of learning algorithms, and the richness of application scenarios make Artificial Intelligence (AI) solution increasingly attractive when solving Geo-spatial Information Science (GSIS) problems. These include image matching, image target detection, change detection, image retrieval, and for generating data models of various types. This paper discusses the connection and synthesis between AI and GSIS in block adjustment, image search and discovery in big databases, automatic change detection, and detection of abnormalities, demonstrating that AI can integrate GSIS. Moreover, the concept of Earth Observation Brain and Smart Geo-spatial Service (SGSS) is introduced in the end, and it is expected to promote the development of GSIS into broadening applications.     

中国对虾人工诱导雌核发育的研究Ⅰ──四步诱导法

１９９０～１９９３年于青岛采用研究鱼类人工诱导雌核发育相类似的方法 ──紫外线照射精子、受精、温度或细胞松胞素Ｂ处理卵子方法，对中国对虾进行 人工雌核发育的研究。实验结果，有三批材料获得较好的效果：二批培育到蚤状幼 体，另一批培育到３～３．５ｃｍ幼虾。经受精细胞学观察，被照射的精子经受精，其核 有的不能原核化，有的虽能原核化，且能向中央迁移，但都不能与卵原核结合。实验 表明，这三批育出的个体是雌核发育的个体。     

秋茄（Kandelia obovata）和无瓣海桑（Sonneratia apetala）红树人工林消波效应量化研究

采用野外监测的方法对海南东寨港三江湾秋茄和无瓣海桑红树人工林的消波效应进行了量化研究。结果表明,裸露海滩的消波作用较弱,波浪在经过裸滩50m后,1/10波高、1/3波高和平均波高分别减低2.9%、4.4%和5.8%。红树林的消波作用显著,总体来说,波浪在经过秋茄林50m后,1/10波高、1/3波高和平均波高分别减低46...     

Simulations of measures to control lava flows

We evaluated the quantitative effects of artificial barriers, water-cooling and guiding channels on lava flow using the lava simulation program LavaSIM. Lava flow is basically subject to the topography around the path, effusive rate and viscosity. To prevent damage due to lava flow, we conducted experiments in controlling the flow direction, velocity and temperature. The simulation demonstrated that artificial barriers can successfully change the direction of a lava flow and is more effective when placed nearly parallel to the flow direction at a point where the topography is not very steep, while a barrier placed perpendicular to the flow direction can only stop the flux temporarily, ultimately allowing the solidified crust to accumulate and causing the following mass to go over the barrier. The water-cooling trial was also effective in controlling the direction and temperature, although the amount of water was as much order as 10⁵ m³. The guiding channels successfully control the direction and inundated area but only in local areas.     

Evaluation of ground vibration effect of blasting operations in a magnesite mine

Ground vibrations induced by blasting are one of the fundamental problems in the mining industry and may cause severe damage to structures and plants nearby. Therefore, a vibration control study plays an important role in the minimization of environmental effects of blasting in mines. This paper presents the results of ground vibration measurement induced by bench blasting at Magnesite Incorporated Company (MAS) open pit mine in Turkey. The scope of this study is to predict peak particle velocity and to determine the slope of the attenuation curve for this site. For this purpose, the blasting parameters of 43 shots were carefully recorded and the ground vibration components were measured for each event. After carrying out statistical analysis, the site specific parameters were determined to predict the peak particle velocity. In the light of this analysis, the prediction graphics of maximum charge weight per delay versus distance for different damage criteria was proposed to be able to perform controlled blasting in order not to damage to the nearby structures, especially to the plant where rotary and shaft kilns have been established.     

无临空面深层岩体爆破增渗模型试验相似律研究及相似条件的实现

依据所分析的无临空面深层岩体爆破增渗的特点,在模型试验相似理论基础上进行无临空面深层岩体爆破增渗模型试验相似律研究。结合内蒙某低渗透砂岩型铀矿工程地质及水文地质实际情况,根据所推导的模型试验相似准则,通过理论分析及试验研究,实现了模型试验的几何条件、边界条件、爆炸荷载及材料的相似,为实施爆破增渗模型试验打下基础。     

初始地应力对隧洞开挖爆生裂隙区的影响研究

隧洞开挖过程中控制爆炸对岩体的损伤,减小爆破裂隙范围,对保证工程安全具有重要意义。基于爆炸应力波作用下爆生裂隙形成机制的研究,采用摩尔-库仑准则及最大拉应力准则,研究了初始地应力对爆炸应力波作用下爆生裂隙区比例半径的影响。研究结果表明,初始地应力对爆生裂隙范围有显著影响。在压剪破坏模式下,爆生裂隙区比例半径随地应力的增大而减小,地应力侧压系数影响爆生裂隙区比例半径在洞壁圆周的分布。考虑围岩应力卸荷影响后,沿隧洞径向的爆生裂隙区比例半径比不考虑卸荷的小。拉伸破坏导致的岩体爆生裂隙区比例半径一般比压剪破坏模式下爆生裂隙比例小,爆炸应力波作用下隧洞围岩更容易沿结构弱面发生压剪破坏。