基于SVM模型的胶东金矿成矿预测研究

本文系统结合前人研究资料，进行控矿要素分析，总结金矿成矿规律，采用支持向量机模型（SVM）进行深部隐伏金矿预测。首先根据研究区矿床类型，得知该区金矿床为造山型，矿床严格受构造、岩浆岩等方面控制。建立了勘查模型，Au、Ag两种贵金属元素经多重分形滤波（S-A）分离得到的背景和异常，进而将各勘查标志转换为证据图层，采用高斯、线性、双曲正切SVM、逻辑回归模型、朴素贝叶斯模型进行成矿预测，最终生成成矿潜力图和累计预测面积比例与累计预测矿床数量图，共圈定远景区5个。结果表明高斯、线性、双曲正切核函数的SVM模型逻辑回归模型相差无几，且均优于证据权模型，表明支持向量机是一种具有广泛的应用前景的成矿预测方法。     

Multimodal inverse perspective mapping

Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints.     

Enhancing river model set-up for 2-D dynamic flood modelling

Flood hazard mapping is a topic of increasing interest involving several aspects in which a series of progress steps have occurred in recent years. Among these, a valuable advance has been performed in solving 2-D shallow water equations in complex topographies and in the use of high resolution topographic data. However, reliable predictions of flood-prone areas are not simply related to these two important aspects. A key element is the accurate set up of the river model. This is primarily related to the representation of the topography but also requires particular attention to the insertion of man-made structures and hydrological data within the computational domain. There is the need to use procedures able to 1) obtain a reliable computational domain, characterized by a total number of elements feasible for a common computing machine, starting from the huge amount of data provided by a LIDAR survey, 2) deal with river reach that receives significant lateral inflows, 3) insert bridges, buildings, weirs and all the structures that can interact with the flow dynamics. All these issues have large effects on the modelled water levels and flow velocities but there are very few papers in the literature on these topics in the framework of the 2-D modelling. So, in this work, attention is focused on the techniques to deal with the above-mentioned issues, showing their importance in flood mapping using two actual case studies in Southern Italy. In particular, the simulations showed in this paper highlight the presence of backwater effects, sudden and numerous changes in the flow regime, induced by the detailed river model, that underline the importance of using 2-D fully dynamic unsteady flow equations for flood mapping.     

Numerical and experimental study of the effect of a froth baffle on flotation cell performance

In this work, the effect of a froth baffle on flotation performance is investigated both experimentally and numerically. Flotation experiments with an artificial ore comprised of 80% silica as gangue and 20% limestone as floatable component were carried out to compare the flotation performance of a baffled froth system against an un-baffled froth system. The effect of the baffle’s inclination angle to the horizontal was also studied. Results indicated that a froth baffle has a profound effect on both recovery and grade. The presence of a froth baffle resulted in an increase in grade at the expense of recovery. The decrease in limestone recovery with the introduction of a froth baffle was found to be a function of the baffle’s inclination angle i.e. recovery decreased as the inclination angle becomes more acute. Water recovery as well as entrainment recovery herein represented by silica recovery decreased with decrease in baffle’s inclination angle. Numerical techniques were employed to model the experimental results. The 2D stream function equation/Laplace equation which is known to be adequate in describing froth transport was solved subject to boundary conditions that represent the presence of baffles. A solution was developed using finite difference methods on a rectangular map obtained using Schwarz–Christoffel (SC) mapping. Results from the simulations indicated a change in particle residence time distribution in a manner that reduces spread. The changes in residence time distribution helped in developing an explanation of the experimental data.     

Analysis of changes in coordinate measuring machines accuracy made by different nodes density in geometrical errors correction matrix

Advances in modern manufacturing techniques increase production efficiency but, at the same time, present new tasks and challenges for coordinate metrology and the manufacturers of Coordinate Measuring Machines (CMMs). The main goal of current research efforts is improving measurement accuracy. Seeing as many of the possible solutions regarding CMM construction had already been explored, there seems to be little left for improvement in that field. Further efforts at accuracy improvement rely mostly on using sophisticated mathematical algorithms designed to correct relevant errors. Many types of errors could be compensated using this approach, including: probe head errors, machine dynamics errors and, most importantly, machine geometrical errors. Almost all coordinate measuring machines produced nowadays are equipped with geometrical errors compensation matrix known as the CAA matrix (Computer Aided Accuracy).CAA matrices are based on a grid of reference points (nodes) in which certain values of the components of geometrical errors are determined experimentally. The error values between the nodes are estimated using simple interpolation methods. Theoretically, a higher density of reference points on the grid describing the CAA matrix should improve the accuracy of the machine utilizing the matrix. On the other hand, increasing the number of nodes simultaneously increases the amount of workload, time and money spent on constructing the CAA matrix. This paper presents a number of experiments aimed at creating CAA matrices with different number of matrix nodes using the LaserTracer system. The relations between maximum permissible errors obtained on a machine using matrices with different densities of nodes are also discussed. Additionally, the authors attempt to tackle the question of determining the most optimal density of nodes with regards to the ratio of time spent on matrix creation and the effect on accuracy.     

Robot semantic mapping through human activity recognition: A wearable sensing and computing approach

Semantic information can help robots understand unknown environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a new robot semantic mapping approach through human activity recognition in a human–robot coexisting environment. An intelligent mobile robot platform called ASCCbot creates a metric map while wearable motion sensors attached to the human body are used to recognize human activities. Combining pre-learned models of activity–furniture correlation and location–furniture correlation, the robot determines the probability distribution of the furniture types through a Bayesian framework and labels them on the metric map. Computer simulations and real experiments demonstrate that the proposed approach is able to create a semantic map of an indoor environment effectively.     

Effect of matrix content on the performance of carbon paper as an electrode for PEMFC

Porous conducting carbon fiber‐based composite paper is used as an electrode backing in the fuel cell assembly. It not only acts as a channel through which the reactant and product gases pass to and from the bipolar plate and the catalyst site but also helps in the flow of electrons. In order to perform its role efficiently, it should have sufficient strength, high electrical conductivity, and ideal porous structure. Carbon paper has been fabricated, which builds up the required composite properties. Studies have been conducted to optimize the fiber/matrix ratio in the carbon paper, while ensuring the perfect combination of porosity, mechanical strength, and electrical conductivity for an electrode in a proton electrolyte membrane fuel cells. Detail physico‐mechanical and electrochemical characterizations further ascertain that the fiber/matrix ratio plays an important role in tuning the composite properties. The polarization curve of the unit proton exchange membrane (PEM) fuel cell (with an effective electrode area 4 cm²) shows a peak power density of 916 mW/cm² for the sample with fiber/matrix ratio of 65:35, which is almost the same as the commercially available sigracet gas diffusion layer (SGL) carbon paper tested under similar conditions. Further, proportionally enlarging the electrode area to 100 cm² shows that the carbon paper not only shows almost repeatable results in a given set up but also scales up.     

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real‐Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel‐like environments. As will be discussed, this type of environment presents several special characteristics that often make well‐established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.     

USAD: undetectable steganographic approach in DCT domain

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.     

Characterization system based on image mapping for field emission devices

Field Emission devices (FE) have been proposed as efficient electron sources for several applications such as electron microscopy and vacuum sensors. Evidently, characterization methods applied during development phase of FE devices are crucial to evaluate aspects related with their working stability, homogeneity, and efficiency. However, the traditional methods provide only overall information about such characteristics, which difficult to improve the performance of these devices and their integration with electronics. To overcome this problem, this work presents an alternative system to characterize FE devices through electron emission imaging in real-time. The proposed system acquires I-V features of FE devices, while a video camera captures the emission image from a phosphor screen. Virtual instrumentation based on LabVIEW manages the whole system including measurement instruments, image capture, and data processing. As a result, histograms, 3D maps, and other FE analyses provide information about emitting characteristics of selected regions of interest. The main contribution of this work is to offer an important tool for the analyses of electron emission, by the association of captured images with the localized emission current. The extracted information from our system can efficiently support the characterization and the development of FE devices.