海南小型露天采石场矿山地质环境治理方法探讨：以日富石场为例

海南小型露天采石场矿山数量多且呈遍地开花式分布，且不规范的开采对环境造成较大的破坏，与绿色矿山之路背道而行，相对大型矿山规范化的治理，小型矿山治理比较滞后，本文以日富采石场地质环境治理为例，通过摸清当地的地质环境条件，结合矿山现状，因地制宜地制定出日富采石场地质环境治理的具体措施，通过治理可获得林地2.6hm2，矿山安全隐患及地貌景观等矿山地质环境问题得到有效地防治，有效解决农业灌溉及周边虾塘养殖用水缺乏问题，实现较好的社会经济效益，为今后治理类似矿山提供参考。     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.     

Maximizing Convergence Speed for Second Order Consensus in Leaderless Multi-Agent Systems

The paper deals with the consensus problem in a leaderless network of agents that have to reach a common velocity while forming a uniformly spaced string. Moreover, the final common velocity (reference velocity) is determined by the agents in a distributed and leaderless way. Then, the consensus protocol parameters are optimized for networks characterized by a communication topology described by a class of directed graphs having a directed spanning tree, in order to maximize the convergence rate and avoid oscillations. The advantages of the optimized consensus protocol are enlightened by some simulation results and comparison with a protocol proposed in the related literature. The presented protocol can be applied to coordinate agents such as mobile robots, automated guided vehicles (AGVs) and autonomous vehicles that have to move with the same velocity and a common inter-space gap.      

Geosynthetic-reinforced soils above voids: Observation and prediction of soil arching

The assumption with the biggest impact on the design of geosynthetic-reinforced soils above voids is the presence and degree of soil arching, which affects the predicted applied stress on the geosynthetic. A series of centrifuge tests were conducted to investigate the soil arching in geosynthetic-reinforced soils with measurements of the soil stresses and observation of soil and geosynthetic deformation used to infer the arching behaviour. Detailed analysis of the results showed that arching significantly reduces the stress at the base of the soil when a void forms; this mechanism is due to stress redistributions and not the formation of a physical arch as suggested in some models. A new method to reliably predict this reduction is proposed by calculating the coefficient of lateral stress on vertical failure planes based on the observations of a continuous convex arc of major principal strains above the void, and the assumption that this is indicative of the stress behaviour.     

Unsupervised regions based segmentation using object discovery

We present a new unsupervised algorithm to discovery and segment out common objects from multiple images. Compared with previous cosegmentation methods, our algorithm performs well even when the appearance variations in the foregrounds are more substantial than those in some areas of the backgrounds. Our algorithm mainly includes two parts: the foreground object discovery scheme and the iterative region allocation algorithm. Two terms, a region-saliency prior and a region-repeatness measure, are introduced in the foreground object discovery scheme to detect the foregrounds without any supervisory information. The iterative region allocation algorithm searches the optimal solution for the final segmentation with the constraints from a maximal spanning tree, and an effective color-based model is utilized during this process. The comparative experimental results show that the proposed algorithm matches or outperforms several previous methods on several standard datasets.     

高层外廊式跃层住宅的探讨

如何使高层住宅成为寻常百姓家，如何降低高层住宅的成本，同时又具有高质量的户型，本文阐述的外 廊式跃层户型提出了一个解决方案。     

黄土力学数据挖掘系统研究

岩土工程智能化系统研究是岩土工程领域前缘问题。本文基于信息技术中最先进的数据挖掘技术,研制了黄土力学数据挖掘系统,包括有预处理模块、挖掘操作模块、知识库管理模块,自重湿陷与湿陷性判定决策树模型、判定规则和评价模型的精度以及其他应用软件接口等。可实现数据的归约、聚类分析、分类、预测等。通过工程实例分析,证明了该系统的有效性和实用性。采用59项工程的2766组黄土试验数据对决策树的每条规则进行测试,结果表明自重湿陷性判定规则的准确率为87.3%,湿陷性判定规则的准确率为92.5%。利用挖掘所得到的判定规则和决策树模型,可减少试验工作、降低成本;提出的模型还可用于确定湿陷底界与自重湿陷的底界,从而为地基处理深度的确定提供依据。