An intelligent robotic vision system with environment perception

Ever since the dawn of computer vision[1, 2], 3D environment reconstruction and object 6D pose estimation have been a core problem. This thesis attempts to develop a novel 3D intelligent robotic vision system integrating environment reconstruction and object detection techniques to solve practical problems. Chapter 2 reviews current state-of-the art of 3D vision techniques from environment reconstruction and 6D pose estimation.In Chapter 3 a novel environment reconstruction system is proposed by using coloured point clouds. The evaluation experiment indicates that the proposed algorithm 2 is effective for small-scale and large scale and textureless scenes. Chapter 4 presents Image-6D (that is section 4.2), a learning-based object pose estimation algorithm from a single RGB image. Contour-alignment is introduced as an efficient algorithm for pose refinement in an RGB image. This new method is evaluated on two widely used benchmark image data bases, LINEMOD and Occlusion-LINEMOD. Experiments show that the proposed method surpasses other state-of-the-art RGB based prediction approaches. Chapter 5 describes Point-6D (defined in section 5.2), a novel 6D pose estimation method using coloured point clouds as input. The performance of this new method is demonstrated on LineMOD [3] and YCB-Video [4] dataset. Chapter 6 summarizes contributions and discusses potential future research directions. In addition, we presents an intelligent 3D robotic vision system deployed in a simulated/laboratory nuclear waste disposal scenario in Appendices B. To verify the results, a simulated nuclear waste handling experiment has been successfully completed via the proposed robotic system

Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor

This paper aims to optimize high-speed railroad timetables for a corridor. We propose an integer programming model using a time-space network-based approach to consider passenger service demands, train scheduling, and station service demands simultaneously. A modified branch-and-price algorithm is used for the computation. This algorithm solves the linear relaxation of all nodes in a branch-and-bound tree using a column generation algorithm to derive a lower-bound value (LB) and derive an upper-bound value (UB) using a rapid branching strategy. The optimal solution is derived by iteratively updating the upper- and lower-bound values. Three acceleration strategies, namely, initial solution iteration, delayed constraints, and column removal, were designed to accelerate the computation. The effectiveness and efficiency of the proposed model and algorithm were tested using Wuhan-Guangzhou high-speed railroad data. The results show that the proposed model and algorithm can quickly reduce the defined cost function by 38.2% and improve the average travel speed by 10.7 km/h, which indicates that our proposed model and algorithm can effectively improve the quality of a constructed train timetable and the travel efficiency for passengers. Document type: Articl

Two-dimensional modeling of the self-limiting oxidation in silicon and tungsten nanowires

AbstractSelf-limiting oxidation of nanowires has been previously described as a reaction- or diffusion-controlled process. In this letter, the concept of finite reactive region is introduced into a diffusion-controlled model, based upon which a two-dimensional cylindrical kinetics model is developed for the oxidation of silicon nanowires and is extended for tungsten. In the model, diffusivity is affected by the expansive oxidation reaction induced stress. The dependency of the oxidation upon curvature and temperature is modeled. Good agreement between the model predictions and available experimental data is obtained. The developed model serves to quantify the oxidation in two-dimensional nanostructures and is expected to facilitate their fabrication via thermal oxidation techniques

Nonlinear stability of a single-layer hybrid grid shell

This paper presents a study of a hybrid grid shell, which is made of quadrangular meshes diagonally stiffened by pre-tensioned thin cables. The construction of the hybrid structure by translating a spatial curve against another spatial curve is firstly described. Then the elasto-plastic buckling analyses of the perfect hybrid structure and the corresponding single-layer lattice shell are carried out, and the influence of the asymmetric load on the failure loads is discussed based on a finite element model. Furthermore, the different shapes and sizes of imperfections are considered in this study. Two schemes of imposing imperfections are chosen: the first several eigenvalue buckling modes and the deformed shape of the loaded structure obtained from a geometrical non-linear analysis are chosen as the imperfection shape. Finally, the effects of different structural parameters, such as the rise-to-span ratio, beam section dimension, area and pre-stress of cables and boundary conditions, on the failure loads are investigated

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with dierent soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data

CDCA2 Inhibits Apoptosis and Promotes Cell Proliferation in Prostate Cancer and Is Directly Regulated by HIF-1α Pathway.

Prostate cancer (PCa) is a major serious malignant tumor and is commonly diagnosed in older men. Identification of novel cancer-related genes in PCa is important for understanding its tumorigenesis mechanism and developing new therapies against PCa. Here, we used RNA sequencing to identify the specific genes, which are upregulated in PCa cell lines and tissues. The cell division cycle associated protein (CDCA) family, which plays a critical role in cell division and proliferation, is upregulated in the PCa cell lines of our RNA-Sequencing data. Moreover, we found that CDCA2 is overexpressed, and its protein level positively correlates with its histological grade, clinical stage, and Gleason Score. CDCA2 was further found to be upregulated and correlated with poor prognosis and patient survival in multiple cancer types in The Cancer Genome Atlas (TCGA) dataset. The functional study suggests that inhibition of CDCA2 will lead to apoptosis and lower proliferation in vitro. Silencing of CDCA2 also repressed tumor growth in vivo. Loss of CDCA2 affects several oncogenic pathways, including MAPK signaling. In addition, we further demonstrated that CDCA2 was induced in hypoxia and directly regulated by the HIF-1α/Smad3 complex. Thus, our data indicate that CDCA2 could act as an oncogene and is regulated by hypoxia and the HIF-1αpathway. CDCA2 may be a useful prognostic biomarker and potential therapeutic target for PCa

Dissociable Modulation of Overt Visual Attention in Valence and Arousal Revealed by Topology of Scan Path

Emotional stimuli have evolutionary significance for the survival of organisms; therefore, they are attention-grabbing and are processed preferentially. The neural underpinnings of two principle emotional dimensions in affective space, valence (degree of pleasantness) and arousal (intensity of evoked emotion), have been shown to be dissociable in the olfactory, gustatory and memory systems. However, the separable roles of valence and arousal in scene perception are poorly understood. In this study, we asked how these two emotional dimensions modulate overt visual attention. Twenty-two healthy volunteers freely viewed images from the International Affective Picture System (IAPS) that were graded for affective levels of valence and arousal (high, medium, and low). Subjects' heads were immobilized and eye movements were recorded by camera to track overt shifts of visual attention. Algebraic graph-based approaches were introduced to model scan paths as weighted undirected path graphs, generating global topology metrics that characterize the algebraic connectivity of scan paths. Our data suggest that human subjects show different scanning patterns to stimuli with different affective ratings. Valence salient stimuli (with neutral arousal) elicited faster and larger shifts of attention, while arousal salient stimuli (with neutral valence) elicited local scanning, dense attention allocation and deep processing. Furthermore, our model revealed that the modulatory effect of valence was linearly related to the valence level, whereas the relation between the modulatory effect and the level of arousal was nonlinear. Hence, visual attention seems to be modulated by mechanisms that are separate for valence and arousal

How Do Farmers Realize Their Rights on the Collective Land in Rural China? An Explanatory Framework for Deconstructing the Subject of Collective Land Ownership

This study aims to deconstruct the collective, the subject of collective land ownership. With respect for the logic of the formation of collective land ownership, we propose the “transfreserve” mode to portray the division of rural land rights between the members and the organization in the transformation from private ownership to collective ownership. This idea can be expressed as, prompted by the public power of the state, each farmer as the owner of rural land having to transfer part of his/her rights to the organization when associating, meanwhile each one still reserves part of his/her rights. We term the rights transferred to the organization as special legal person ownership, while the rights reserved by each farmer are called membership rights. The rights exercised by all members on the basis of membership rights are the autonomous rights. In terms of the property rights, such as the distribution right of the collective income, farmers have to participate in decision-making to determine how to form the allocation scheme in a fair and reasonable way by exercising autonomous rights; then, organization fulfills the collective will to meet the needs of its members by exercising special legal person ownership. As for the right to use public infrastructure on the collective land, farmers, as the members, can use it reasonably by its own will, which is the process of exercising membership rights. If farmers’ rights are infringed by other members, they can choose to negotiate with other members in a proper way. If farmers’ rights are infringed when the organization carries out operation activity in the land market, they can obtain compensation from the organization, and the compensation standard is determined by the decision-making