CAN及其在轿车中央控制系统中的应用

控制器局域网络(CAN)是一种串行数据通信总线,由于其具备可靠性、健壮性和良好的效能价格比,因而在汽车信息控制中得到广泛的应用。以轿车撞车事件为例研究CAN及其在轿车中央控制系统中的应用,旨在提高轿车性能。

Characterizing historical(1992–2010) transitions between grassland and cropland in mainland France through mining land-cover survey data

Grassland, as one of the largest ecosystems on the earth, supports various goods and services to humanity.Historically, humans have increased agricultural output primarily by cropland expansion and agricultural intensification.The cropland area was primarily gained at the expense of grassland and forests.Apart from grassland conversion, increasing consumption of calorie- and meat-intensive diets drives the intensification of livestock systems, which is shifting steadily from grazing to feeding with crops.To cope with the environmental degradation due to agriculture, various forms of ‘green payment＇ were implemented to promote the adoption of sustainable farming practices over the last two decades in the European Union.The aim of this study is to monitor the recent transitions（1992–2010） between grassland and cropland during two Common Agricultural Policy（CAP） reforms at the French mainland scale.We proposed an innovative approach to link grassland conversion to agricultural commodities and farming systems practices.We first assessed the grassland-to-cropland conversion and further investigated the crop sequence patterns that were observed to be dominant after the conversion through mining land-cover survey data Teruti and Teruti-Lucas.We found the trends of the transitions between grassland and cropland over the two time intervals： The loss of grassland（1992–2003） and restoration or re-expansion of grassland（2006–2010） in mainland France.Our finding on the crop sequence patterns after the grassland conversion reveals two notable evolutions of agricultural production systems.These evolutions were related to the increase in the proportion of cropland in the total agricultural land use.One evolution was most likely influenced by the demand for fodder： The conversion from grazing livestock to feeding livestock.Another evolution was the conversion from livestock production to field crop production.Our results indicate that the intensification of livestock farming systems continued over the last two decades in France.We conclude that, the approach developed in this study can be considered as a generic method for monitoring the transitions between grassland and cropland and further identifying the crop sequence patterns after the grassland conversion from time-series land cover data.

不确定性对策略性投票的影响（英文）

如果选民不确定其他选民的偏好以及其他选民对他本人偏好的知晓程度,选票操纵的策略会变得更复杂。为了研究这种认知不确定性对策略性投票的影响,本文提供了一个形式分析框架并定义了选票操纵、优势偏好、均衡票况等概念。我们发现:即使真实偏好相同但关于偏好的高阶知识不同也会影响均衡票况。另外,本文还研究了不确定性的更新对选票操纵的影响,如某位选民对他本人的偏好进行公开宣告。我们证明了有些形式的选票操纵和均衡票况可以在这样的更新下得到保持,而另一些则不能被保持。最后,我们用一个逻辑语言形式化了不确定性选举的相关概念。本文的目的是给出一个分析和设计面向不确定性的投票机制的技术基础。

Anomaly Detection in Textured Images with a Convolutional Neural Network for Quality Control of Micrometric Woven Meshes

Industrial woven meshes are composed of metal materials and are often used in construction,industrial and residential activities or applications.The objective of this work is defect detection in industrial fabrics in the quality control stage.In order to overcome the limitations of manual methods,which are often tedious and time-consuming,we propose a strategy that can automatically detect defects in micrometric steel meshes by means of a Convolutional Neural Network.The database used for such a purpose comes from real problem data for anomaly detection in micrometric woven meshes.This detection is performed through supervised classification with a Convolutional Neural Network using a VGG19 architecture.We define a pipeline and a strategy to tackle the related small amount of data.It includes i)augmenting the database with translation,rotation and symmetry,ii)using pre-trained weights and iii)checking the learning curve behaviour through cross-validation.The obtained results show that,despite the small size of our databases,detection accuracy of 96%was reached.

Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening

Fusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHB occurs in Europe, North America and around the world causing significant losses in production and endangers human and animal health. In this article, we provide the strategic steps for the specific target selection for the phytopathogen system wheat-Fusarium graminearum. The economic impact of FHB leads to the need for innovation. Currently used fungicides have been shown to be effective over the years, but recently cereal infecting Fusaria have developed resistance. Our work presents a new perspective on target selection to allow the development of new fungicides. We developed an innovative approach combining both genomic analysis and molecular modeling to increase the discovery for new chemical compounds with both safety and low environmental impact. Our protein targets selection revealed 13 candidates with high specificity, essentiality and potentially assayable with a favorable accessibility to drug activity. Among them, three proteins: trichodiene synthase, endoglucanase-5 and ERG6 were selected for deeper structural analyses to identify new putative fungicides. Overall, the bioinformatics filtering for novel protein targets applied for agricultural purposes is a response to the demand for chemical crop protection. The availability of the genome, secretome and PHI-base allowed the enrichment of the search that combined experimental data in planta. The homology modeling and molecular dynamics simulations allowed the acquisition of three robust and stable conformers. From this step, approximately ten thousand compounds have been virtually screened against three candidates. Forty-five top-ranked compounds were selected from docking results as presenting better interactions and energy at the binding pockets and no toxicity. These compounds may act as inhibitors and lead to the development of new fungicides.

How We Will Discover Sentience in AI

This paper explores the question of how we can know if Artificial Intelligence(AI)systems have become or are becoming sentient.After an overview of some arguments regarding AI sentience,it proceeds to an outline of the notion of negation in the philosophy of Josiah Royce,which is then applied to the arguments already presented.Royce’s notion of the primitive dyadic and symmetric negation relation is shown to bypass such arguments.The negation relation and its expansion into higher types of order are then considered with regard to how,in small variations of active negation,they would disclose sentience in AI systems.Finally,I argue that the much-hyped arguments and apocalyptic speculations regarding Artificial General Intelligence(AGI)takeover and similar scenarios,abetted by the notion of unlimited data,are based on a fundamental misunderstanding of how entities engage their experience.Namely,limitation,proceeding from the symmetric negation relation,expands outward into higher types of order in polyadic relations,wherein the entity self-limits and creatively moves toward uniqueness.

Deep learning for the detection of semantic features in tree X-ray CT scans

According to the industry,the value of wood logs is heavily influenced by their internal structure,particularly the distribution of knots within the trees.Nowadays,CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees.Knowing where the tree semantic features,especially knots,contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products.However,this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline,knot localization and log contour,in a robust and efficient manner.In this paper,we propose an effective methodology based on deep learning for performing these different tasks by processing CTscanner images with deep convolutional neural networks.To meet this objective,three end-to-end trainable pipelines are proposed.The first pipeline is focused on centers detection using CNNs architecture with a regression head,the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture.The different architectures are tested on several tree species.With these experiments,we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness.The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

FAIREST:A Framework for Assessing Research Repositories

The open science movement has gained significant momentum within the last few years.This comes along with the need to store and share research artefacts,such as publications and research data.For this purpose,research repositories need to be established.A variety of solutions exist for implementing such repositories,covering diverse features,ranging from custom depositing workflows to social media-like functions.In this article,we introduce the FAIREST principles,a framework inspired by the well-known FAIR principles,but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts.The goal is to support decision makers in choosing such a solution when planning for a repository,especially at an institutional level.The metrics included are therefore based on two pillars:(1)an analysis of established features and functionalities,drawn from existing dedicated,general purpose and commonly used solutions,and(2)a literature review on general requirements for digital repositories for research artefacts and related systems.We further describe an assessment of 11 widespread solutions,with the goal to provide an overview of the current landscape of research data repository solutions,identifying gaps and research challenges to be addressed.

中国科学学40年研究主题变迁--基于特征最大化F指标的文本内容分析

基于F指标的特征最大化的GNG聚类方法,对科学学研究文献文本进行内容分析,绘制了中国科学学近40年的研究主题结构图谱,并附以论文发表时间和作者辅助信息的外生标签梳理出中国科学学研究主题的变迁。这种结合了F指标特征最大化无监督学习方法的分析结果显示科学学研究在近40年逐渐走向成熟,从学科一般属性探讨转向相关学科与知识结构分析,从定性分析转向偏重于定量分析和可视化分析,从科学的一般社会功能研究转向更为具体的经济功能和战略功能研究。

Flocking control of a fleet of unmanned aerial vehicles

Current applications using single unmanned vehicle have been gradually extended to multiple ones due to their increased efficiency in mission accomplishment, expanded coverage areas and ranges, as well as enhanced system reliability. This paper presents a flocking control method with application to a fleet of unmanned quadrotor helicopters （UQHs）. Three critical characteristics of formation keeping, collision avoidance, and velocity matching have been taken into account in the algorithm development to make it capable of accomplishing the desired objectives （like forest/pipeline surveillance） by safely and efficiently operating a group of UQHs. To achieve these, three layered system design philosophy is considered in this study. The first layer is the flocking controller which is designed based on the kinematics of UQH. The modified Cucker and Smale model is used for guaranteeing the convergence of UQHs to flocking, while a repelling force between each two UQHs is also added for ensuring a specified safety distance. The second layer is the motion controller which is devised based on the kinetics of UQH by employing the augmented state-feedback control approach to greatly minimize the steady-state error. The last layer is the UQH system along with its actuators. Two primary contributions have been made in this work： first, different from most of the existing works conducted on agents with double integrator dynamics, a new flocking control algorithm has been designed and implemented on a group of UQHs with nonlinear dynamics. Furthermore, the constraint of fixed neighbouring distance in formation has been relaxed expecting to significantly reduce the complexity caused by the increase of agents number and provide more flexibility to the formation control. Extensive numerical simulations on a group of UQH nonlinear models have been carried out to verify the effectiveness of the proposed method.