Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.     

Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield

Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty; characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold deposits, the classification of the neural network results provides an improved prediction of the distribution of the more important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration.     

面向电网巡检的多旋翼无人机航测系统关键技术研究及应用

针对无人机电力巡检所面临的定位精度低、近距离巡检操控难度大、自动化程度低、人为操控事故风险高等问题,本文提出了利用多旋翼无人机RTK差分定位技术、复杂塔型飞行规则机器学习算法等无人机航测系统技术,研发面向电网巡检的无人机智能操作系统及控制终端,提升电网线路日常管理和应急指挥的规范性、便利性和经济性,为电网管理部门提供一种低成本、易操作、高效率、机动灵活的新型巡检模式。     

翻转课堂在地理信息系统课程教学中的实践与思考

针对教师主导的传统教学模式在地理信息系统课程教学中存在的不足,提出了将翻转课堂这一新的混合学习教学模式引入教学的想法。首先分析了地理信息系统课程实施翻转课堂的必要性和可行性,然后在综合考虑地理信息系统课程特点与中外教育文化差异的基础上进行了课程设计和组织实施。实践结果表明,实施翻转课堂确实能够提高学生学习主动性和教学质量,同时也对教师的专业水平和教学能力提出了更高的要求。     

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.     

Charting the emergence of a ‘knowing system’ for climate change adaptation in Australian regional natural resource management

Climate change increases the complexity and uncertainty of regional natural resource management (NRM), calling into question the appropriateness of linear knowledge-transfer approaches. In this paper we reflect on knowledge practices among a partnership of researchers and NRM planners, under a federal program of NRM investment intended to ‘deliver information’ to regional NRM planners to support planning for climate change. We unpack ‘container’ and ‘conduit’ metaphors of linear, one-way communication invoked by the starting conditions, and explore whether more relational ways of communicating were achieved. A key theme emerged early in the research that NRM planners felt overwhelmed by the sheer volume of information available and discouraged by the irrelevance of much of it to their climate change planning. Our research-practice collaboration unfolded in this context and through ongoing face-to-face and virtual engagement over a period of two years. The collaborative approach featured joint identification of priority activities, co-design of planning approaches, and the iterative co-development of an online ‘information portal’, which acted as a boundary object. We report the emergence of a ‘knowing system’, resulting from these efforts to foster relationships and co-produce boundary objects in a particular geographic context. Our findings highlight the potential benefits of investing in the capacity of researchers and NRM practitioners to engage in collaborative research partnerships premised on the emergence of knowing systems.     

基于深度学习的区域生态安全时空模拟与预测

区域作为人类、自然、社会共同作用和互相影响的复杂系统,对区域进行生态量化建模与模拟仿真,是实现区域可持续发展战略的关键。传统机器学习方法对区域生态系统建模取得了一定的成果,但难以确定学习特征和实现时空模拟。深度学习不需事先确定训练特征,具有优异的特征学习能力,能够提高模型预测精度,因此利用深度学习进行建模具有显著优势。本文使用植被净初级生产力（NPP）、气溶胶光学厚度（AOD）和人口格网数据,充分利用深度学习的优点,采用最优深度神经网络时空模拟,得到了河南省2007-2014年3 km分辨率的生态赤字空间分布图和河南省2015-2020年的生态赤字时间预测结果并进行分析,为区域生态的科学管理和建设供科学依据和参考。     

基于在线学习与误差修正的珠江河口区盐度预报

受潮汐、径流、风速风向、地形变化等多种海陆要素交互作用,河口区盐水入侵呈高度不确定性与非线性特征,盐度预报难度较大.利用在线学习算法与误差自回归修正方法在水文预报中时效性更强的优点,构建一种耦合在线序列极限学习机-误差修正(OSELM-EC)盐水入侵预报模型,选取珠江河口区磨刀门水道为典型研究区进行逐日盐度预报.结果表...     

大学新生心理控制源、学习动机与学业成绩的关系

以广东海洋大学2009级53个专业1 876名(男生1 156人,女生720人)新生为对象,研究心理控制源、学习动机、学业成绩的特点及相互关系。研究结果表明:1)大多数新生的心理控制源为内控,没有显著的性别差异;2)学习动机多元化趋势明显,有非常显著的性别差异,男生学习动机更强;3)学业成绩有非常显著的性别差异,女生学业成绩更好;4)心理控制源、学习动机、学业成绩之间存在明确关系,心理控制源与学习动机、心理控制源与学业成绩有非常显著负相关,学习动机与学业成绩有非常显著正相关;5)学业成绩受心理控制源、学习动机等综合影响。     

元认知理论框架下大学英语教学与测试模式探析

元认知是人们对自身认知活动的自我意识、自我监控与自我调节,元认知理论在学习实践中具有广泛的适应性。大学英语教学中运用元认知策略改革教学与测试模式,能提高学生的自主学习能力,取得良好的教学效果。