查看更多>>摘要：Optimal trajectory planning is a funda-mental problem in the area of robotic research.On the time-optimal trajectory planning problem during the mo-tion of a robotic arm,the method based on segmented polynomial interpolation function with a locally chaotic particle swarm optimization(LCPSO)algorithm is pro-posed in this paper.While completing the convergence in the early or middle part of the search,the algorithm steps forward on the problem of local convergence of tradition-al particle swarm optimization(PSO)and improved learning factor PSO(IFPSO)algorithms.Finally,simula-tion experiments are executed in joint space to obtain the optimal time and smooth motion trajectory of each joint,which shows that the method can effectively shorten the running time of the robotic manipulator and ensure the stability of the motion as well.
查看更多>>摘要：Adopt software-definition technology to decouple the functional components of the industrial con-trol system(ICS)in a service-oriented and distributed form is an important way for the industrial Internet of things to integrate information technology,communica-tion technology,and operation technology.Therefore,this paper presents the concept of software-defined control ar-chitecture and describes the time consistency require-ments under the paradigm shift of ICS architecture.By analyzing the physical clock and virtual clock mechanism models,the global clock synchronization space is logically divided into the physical and virtual clock synchroniza-tion domains,and a formal description of the global clock synchronization space is proposed.According to the fun-damental analysis of the clock state model,the physical clock linear filtering synchronization model is derived,and a distributed observation fusion filtering model is con-structed by considering the two observation modes of the virtual clock to realize the time synchronization of the global clock space by way of timestamp layer-by-layer transfer and fusion estimation.Finally,the simulation res-ults show that the proposed model can significantly im-prove the accuracy and stability of clock synchronization.
查看更多>>摘要：With the recent increase in the number of Internet of things(IoT)services,an intelligent schedul-ing strategy is needed to manage these services.In this paper,the problem of automatic choreography of mi-croservices in IoT is explored.A type of reinforcement learning(RL)algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network.The optimal policy is gradu-ally reached during the learning procedure to achieve the goal,despite the dynamic characteristics of the network environment.The simulation results show that compared with other methods,the TD3 algorithm converges faster after a certain number of iterations,and it performs bet-ter than other non-RL algorithms by obtaining the highest reward.The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.
查看更多>>摘要：Phrase-indexed question answering(PIQA)seeks to improve the inference speed of question answering(QA)models by enforcing complete independ-ence of the document encoder from the question encoder,and it shows that the constrained model can achieve sig-nificant efficiency at the cost of its accuracy.In this pa-per,we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models.We propose a novel framework—AnsDR,which consists of an answer boundary detector(AnsD)and an answer candidate ranker(AnsR).More specific-ally,AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundar-ies;and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the effi-ciency.We perform the extensive experiments on public datasets.The experimental results show that the pro-posed method achieves the state of the art on the PIQA task.
查看更多>>摘要：Existing neural approaches have achieved significant progress for Chinese word segmentation(CWS).The performances of these methods tend to drop dramatically in the cross-domain scenarios due to the data distribution mismatch across domains and the out of vocabulary words problem.To address these two issues,proposes a lexicon-augmented graph convolutional net-work for cross-domain CWS.The novel model can cap-ture the information of word boundaries from all candid-ate words and utilize domain lexicons to alleviate the dis-tribution gap across domains.Experimental results on the cross-domain CWS datasets(SIGHAN-2010 and TCM)show that the proposed method successfully models in-formation of domain lexicons for neural CWS approaches and helps to achieve competitive performance for cross-domain CWS.The two problems of cross-domain CWS can be effectively solved through various interactions between characters and candidate words based on graphs.Further,experiments on the CWS benchmarks(Bakeoff-2005)also demonstrate the robustness and efficiency of the proposed method.
LIN JingjingYE ZhonglinZHAO HaixingFANG Lusheng...
958-968页
查看更多>>摘要：With the development of deep learning,graph neural networks(GNNs)have yielded substantial results in various application fields.GNNs mainly con-sider the pair-wise connections and deal with graph-struc-tured data.In many real-world networks,the relations between objects are complex and go beyond pair-wise.Hypergraph is a flexible modeling tool to describe intric-ate and higher-order correlations.The researchers have been concerned how to develop hypergraph-based neural network model.The existing hypergraph neural networks show better performance in node classification tasks and so on,while they are shallow network because of over-smoothing,over-fitting and gradient vanishment.To tackle these issues,we present a novel deep hypergraph neural network(DeepHGNN).We design DeepHGNN by using the technologies of sampling hyperedge,residual connection and identity mapping,residual connection and identity mapping bring from graph convolutional neural networks.We evaluate DeepHGNN on two visual object datasets.The experiments show the positive effects of DeepHGNN,and it works better in visual object classific-ation tasks.
查看更多>>摘要：The weighted sampling methods based on k-nearest neighbors have been demonstrated to be ef-fective in solving the class imbalance problem.However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighbor-hood when calculating sample weight.This paper pro-poses a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging al-gorithm's performance on imbalanced datasets.It con-siders the positional relationship between the center sample and the heterogeneous samples in its neighbor-hood when identifying critical samples.And a parameter reduction method is proposed and combined into the en-semble learning framework,which reduces the paramet-ers and increases the classifier's diversity.We compare NWBBagging with some state-of-the-art ensemble learn-ing algorithms on 34 imbalanced datasets,and the result shows that NWBBagging achieves better performance.
查看更多>>摘要：Since the basic probability of an interval-valued belief structure(IBS)is assigned as interval num-ber,its combination becomes difficult.Especially,when dealing with highly conflicting IBSs,most of the existing combination methods may cause counter-intuitive results,which can bring extra heavy computational burden due to nonlinear optimization model,and lose the good property of associativity and commutativity in Dempster-Shafer theory(DST).To address these problems,a novel con-flicting IBSs combination method named CSUI(conflict,similarity,uncertainty,intuitionistic fuzzy sets)-DST method is proposed by introducing a similarity measure-ment to measure the degree of conflict among IBSs,and an uncertainty measurement to measure the degree of dis-cord,non-specificity and fuzziness of IBSs.Considering these two measures at the same time,the weight of each IBS is determined according to the modified reliability de-gree.From the perspective of intuitionistic fuzzy sets,we propose the weighted average IBSs combination rule by the addition and number multiplication operators.The ef-fectiveness and rationality of this combination method are validated with two numerical examples and its applica-tion in target recognition.
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