查看更多>>摘要：This study mainly focused on the dynamic self-similar kc-center network as a result of information distribution through social networks.Individual attraction with various preferences was characterized in the model as a result of reciprocal attraction among individuals and human multi-attribute.Additionally,the model incorporated the community network structure and network evolution mechanism,and a dynamic self-similar kc-center network generation model was presented.Compared with the classical scale-free network generation algorithm,the generated network embodied not only the characteristics of the small-world and scale-free,but also the characteristics of dynamic self-similar kc-center network.The experimental results were verified by comparing the real data with the experimental data.The results showed that there are dynamic self-similar kc-center networks and their internal network relationship dynamics in the micro scale,meso scale and global perspective based on information dissemination.
查看更多>>摘要：The inbound and outbound tasks for valuable imported ship outfitting items are operated by multiple automated guided vehicles(AGVs)simultaneously in the outfitting warehouse.Given the efficiency mismatch between transportation equipment and the lack of effective scheduling of AGVs,the objective of the studied scheduling problem is to minimize the total travel time cost of vehicles.A multi-AGV task scheduling model based on time window is established considering the loading constraints of AGVs and cooperation time window constraints of stackers.According to the transportation characteristics in the outfitting warehouse,this study pro-poses a conflict detection method for heavy forklift AGVs,and correspondingly defines a conflict penalty function.Furthermore,to comprehensively optimize travel time cost and conflict penalty,a hybrid genetic neighborhood search algorithm(GA-ANS)is proposed.Five neighborhood structures are designed,and adaptive selection opera-tors are introduced to enhance the ability of global search and local chemotaxis.Numerical experiments show that the proposed GA-ANS algorithm can effectively solve the problem even when the scale of the problem increases and the effectiveness of the vehicle conflict penalty strategy is analyzed.
查看更多>>摘要：Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every field where data need to be uploaded to the cloud.Federated learning(FL)is an emerging trend for distributed training of data.The primary goal of FL is to train an efficient communication model without compromising data privacy.The traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic data.This paper presents FL-based traffic flow prediction with spatio-temporal correlation.This work uses a differential privacy(DP)scheme for privacy preservation of participant's data.To the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation.The proposed framework trains the data locally at the client-side with DP.It then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained models.The results of the proposed work show that the FedGCN model accurately predicts the traffic.DP scheme at client-side helps clients to set a budget for privacy loss.
查看更多>>摘要：To avoid colliding with trees during its operation,a lawn mower robot must detect the trees.Existing tree detection methods suffer from low detection accuracy(missed detection)and the lack of a lightweight model.In this study,a dataset of trees was constructed on the basis of a real lawn environment.According to the theory of channel incremental depthwise convolution and residual suppression,the Embedded-A module is proposed,which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model.According to residual fusion theory,the Embedded-B module is proposed,which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion.The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions.Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17％and 69.91％average precision values respectively for trunk and spherical tree,and 77.04％mean average precision value.The number of convolution parameters is 1.78 x 106,and the calculation amount is 3.85 billion float operations per second.The size of weight file is 7.11MB,and the detection speed can reach 179 frame/s.This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots.
查看更多>>摘要：Conforming to the rapidly increasing market demand of crack detection for tall buildings,the idea of integrating deep network technology into wall-climbing robot for crack detection is put forward in this paper.Taking the dependence and hardware requirements when deployed on such edge devices as Raspberry Pi into consideration,the Darknet neural network is selected as the basic framework for detection.In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks,the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly.The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets,which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files.Meanwhile,the AlexeyAB version of Darknet accelerated by NNpack package is deployed on Raspberry Pi 4B,and the crack detection experiments are carried out.Some characteristics,e.g.,fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm,are confirmed by comparison with those of original YOLOv4-tiny algorithm.The innovations of this paper focus on the simple network structure,fewer network layers,and earlier forward transmission of features to prevent over-fitting,showing the new lite neural network exceeds the original YOLOv4-tiny network significantly.
查看更多>>摘要：Assembly process documents record the designers'intention or knowledge.However,common knowl-edge extraction methods are not well suitable for assembly process documents,because of its tabular form and unstructured natural language texts.In this paper,an assembly semantic entity recognition and relation con-struction method oriented to assembly process documents is proposed.First,the assembly process sentences are extracted from the table through concerned region recognition and cell division,and they will be stored as a key-value object file.Then,the semantic entities in the sentence are identified through the sequence tagging model based on the specific attention mechanism for assembly operation type.The syntactic rules are designed for realizing automatic construction of relation between entities.Finally,by using the self-constructed corpus,it is proved that the sequence tagging model in the proposed method performs better than the mainstream named entity recognition model when handling assembly process design language.The effectiveness of the proposed method is also analyzed through the simulation experiment in the small-scale real scene,compared with manual method.The results show that the proposed method can help designers accumulate knowledge automatically and efficiently.
查看更多>>摘要：Graphs are used as a data structure to describe complex relationships between things.The node classification method based on graph network plays an important role in practical applications.None of the existing graph node classification methods consider the uneven distribution of node labels.In this paper,a graph convolution algorithm on a directed graph is designed for the distribution of unbalanced graph nodes to realize node classification based on multi-scale fusion graph convolution network.This method designs different propagation depths for each class according to the unbalance ratio on the data set,and different aggregation functions are designed at each layer of the graph convolutional network based on the class propagation depth and the graph adjacency matrix.The scope of information dissemination of positive samples is expanded relatively,thereby improving the accuracy of classification of unbalanced graph nodes.Finally,the effectiveness of the algorithm is verified through experiments on the public text classification datasets.
查看更多>>摘要：In software development projects,bugs are common phenomena.Developers report bugs in open source repositories.There is a need to develop high quality developer prediction model that considers developer work satisfaction,keep within limited development cost,and improve bug resolution time.To address and resolve bug report as soon as possible is the main focus of triager when a new bug is reported.Thus,developer work efficiency is an important factor in bug-fixing.To address these issues,a proposed approach recommends a set of developers that could potentially share their knowledge with each other to fix new bug reports.The proposed approach is called developer working efficiency and social network based developer recommendation(DweSn).It is a composite model that builds developers'profile by using developer average bug fixing time,work efficiency to fix variety of bugs,as well as the developer's social interactions with other developers.A similarity measure is applied between new bug and bugs in corpus to extract the list of capable developers from the corpus.The proposed approach only selects those developers who are active and less loaded with work.The developer with the highest profile score is assigned the bugs.We evaluated our approach on the subset of five large open-source projects including Mozilla,Netbeans,Eclipse,Firefox and OpenOffice,and compared it with the state-of-the-art.The results demonstrate that combination of developers'efficiency with their average bug fixing time and interactions in their social network gives good accuracy and efficiently reduces bug tossing length.This approach shows an improvement in prediction accuracy,precision,recall,F-score and reduced bug tossing length up to 93.89％,93.12％,93.46％,93.27％and 93.25％,respectively.The proposed approach achieved a 93％hit ratio and 93.34％mean reciprocal rank,indicating that our proposed triager is able to efficiently assign bugs to correct developers.
查看更多>>摘要：In order to analyze the trustworthiness of complex software systems,we propose a model of evidence-based software trustworthiness called trustworthiness derivation tree(TDT).The basic idea of constructing a TDT is to refine main properties into key ingredients and continue the refinement until basic facts such as evidences are reached.The skeleton of a TDT can be specified by a set of rules,which are convenient for automated reasoning in Prolog.We develop a visualization tool that can construct the skeleton of a TDT by taking the rules as input,and allow a user to edit the TDT in a graphical user interface.In a software development life cycle,TDTs can serve as a communication means for different stakeholders to agree on the properties about a system in the requirement analysis phase,and they can be used for deductive reasoning so as to verify whether the system achieves trustworthiness in the product validation phase.We have piloted the approach of using TDTs in more than a dozen real scenarios of software development.Indeed,using TDTs helped us to discover and then resolve some subtle problems.
查看更多>>摘要：Software trustworthiness includes many attributes.Reasonable weight allocation of trustworthy at-tributes plays a key role in the software trustworthiness measurement.In practical application,attribute weight usually comes from experts'evaluation to attributes and hidden information derived from attributes.Therefore,when the weight of attributes is researched,it is necessary to consider weight from subjective and objective as-pects.First,a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process(FAHP)method and the criteria importance though intercrieria correlation(CRITIC)method.Second,based on the weight allocation method,the trustworthiness measurement models of component-based software are estab-lished according to the seven combination structures of components.Third,the model reasonability is verified via proving some metric criteria.Finally,a case is carried out.According to the comparison with other models,the result shows that the model has the advantage of utilizing hidden information fully and analyzing the com-bination of components effectively.It is an important guide for measuring the trustworthiness measurement of component-based software.
NETL
NSTL
万方数据