Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.     

基于支持向量回归和贝叶斯优化的海岸线预测

模拟预测海岸线的动态变化对于沿海地带的开发和科学管理有较重要意义。在获取围头湾研究区Landsat-8卫星时序影像和验潮站数据的基础上,利用支持向量回归模型,结合贝叶斯优化获取最佳超参数,进而预测得到未来年份的海岸线数据,并采用ROC匹配、平均偏移量和均方根误差3种方法验证预测得到的海岸数据。结果表明：基于贝叶斯优化的支持向量回归模型,可以较好地预测围头湾区域未来海岸线,对应的完整度、正确度和质量分别为87.06%、86.10%和80.05%,平均偏移量和均方根误差分别为0.64像素和0.96像素;对于自然岸线预测结果较好,而人工岸线预测部分结果较差。     

水产饲料膨化机设计刍论

据开发应用水产饲料膨化机实践，初步论述了膨化机生产能力与动力匹配，螺杆设计参数长径比，物料在腔内滞留时间，膨化腔设计及其温度调控，并提出改进设计的见解.     

海岸水边线提取方法在GF-2卫星影像中的适应性研究

准确提取海岸水边线是海岸线变迁研究中必不可少的基本工作。本文以海南省陵水黎族自治县2016年的GF-2影像为数据源,在大气校正和正射校正等预处理的基础上,结合数学形态学处理等方法,采用NDWI (Normalized Difference Water Index)阈值法、ISOADTA (Iterative Self-Organizing Data Analysis Technique Algorithm)、SVM (Support Vector Machine)和面向对象法（Object-Oriented Classification,OOC）等4种不同方法来对海岸水边线进行提取,并对其提取结果进行定性和定量评估,进而分析比较了这4种方法在提取人工海岸、砂质海岸和基岩海岸等3种不同海岸类型的水边线时的提取效果和提取精度。结果表明,SVM方法在3种海岸水边线上的总体提取效果最好,总体提取精度最高。     

八段锦联合同伴支持管理对COPD稳定期肺康复的效果评价

目的：探讨八段锦联合同伴支持管理对慢性阻塞性肺疾病（COPD）稳定期患者肺康复依从性及BODE指数的影响。方法：选择69 例由COPD急性加重进入稳定期的患者为研究对象,采用随机数字表法分为观察组（35 例）和对照组（34 例）。对照组给予常规护理、肺康复指导及发放康复处方执行记录单,观察组在对照组基础上采用八段锦运动训练,建立同伴支持管理微信群随访干预。比较2组患者肺康复依从性和BODE指数的变化情况。结果：干预后,观察组肺康复量表各项指标（除BMI）评分及总分、BODE指数各项指标及总分均优于对照组,差异有统计学意义(P＜0.01)。结论：慢性阻塞性肺疾病稳定期在肺康复过程中应用八段锦联合同伴支持管理,能激发患者康复依从性,改善BODE指数,提高患者生活质量。     

基于SVM的海底声纳图像底质识别*

对海底底质声纳图像分割,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,优化支持向量机(SVM)参数.利用多分类 SVM 对砂、礁石、泥进行训练,有效、快速地分离出了3种底质类型,测试精度高于80%,表明该方法可有效用于海底底质声纳图像的识别和分类.     

百合病因机证治浅析

《金匮要略》是目前我国最早的一部诊断治疗杂病的医学专著,书中所提“百合病”仅有9条,内容却涵盖了百合病的病因病机、主症、治则等诸多方面,为后世医家诊断和治疗情志内伤疾病建立了坚实的理论基础和临床思路。本文对《金匮要略》所提百合病及该病治法进行简单分析探讨,以期进一步领悟张仲景辨证施治的思想精髓。     

Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit

Due to the geological complexities of ore body formation and limited borehole sampling, this paper proposes a robust weighted least square support vectormachine (LS-SVM) regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each in complete sample. Then, a weighted K-nearest neighbor (WKNN) algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimization (PSO) algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by randomdivision. Coupled simulated annealing (CSA) and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight (IDW), ordinary kriging (OK), and back propagation (BP) neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the othermethods, and has strong predictive and generalization ability.     

世界海洋国家海上巡逻机主要机型及其搜索设备概述

我国海洋维权形势日益严峻,与周边国家海洋和岛屿主权权益争端不断。现代海上巡逻机速度快、活动半径大、机动性好,对海洋权益维护、海洋环境监测等具备远程快速反应能力。文章介绍了美国、日本、韩国、俄罗斯、越南、菲律宾、马来西亚等国家海上巡逻机的主要机型及其配备的搜索设备,为我国海上巡逻机的建设和发展提供参考借鉴。