A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aided multi-model tracking method for maneuvering targets is proposed.

乙烯基吡咯烷酮/乙烯基咪唑聚合物的制备及其防沾染性

以N-乙烯基吡咯烷酮(NVP)和N-乙烯基咪唑(NVI)为原料合成了三种不同单体比例的N-乙烯基吡咯烷酮/N-乙烯基咪唑聚合物(VP/VI聚合物),采用乌氏黏度计、傅里叶变化红外光谱(FT-IR)、凝胶渗透色谱(GPC)对聚合物的结构进行了表征,结果表明三种聚合物的K值均在30左右,重均分子量(M_(W))分别为37 848,22 656,8 111。通过紫外可见光光谱(UV-Vis)和防沾染实验对三种VP/VI聚合物的防沾染性进行测试,结果表明,加入VPVI-P,VPVI-5和VPVI-7后,直接大红染液的最大吸收波长(λ_(max))从499 nm分别升至520,527和531 nm,λ_(max)处的吸光度从0.30升至0.39左右;直接深蓝染液的λ_(max)从558 nm升至576 nm左右,λ_(max)处的吸光度从0.15升至0.35,三种聚合物均与直接大红、直接深蓝染料分子有明显相互作用。随着聚合物中NVI组分的增加,VP/VI聚合物的防串色性能得到提升。

A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs

Dear Editor,This letter is concerned with the energy-aware multiple sensor coscheduling for bearing-only target tracking in the underwater wireless sensor networks(UWSNs).Considering the traditional methods facing with the problems of strong environment dependence and lack flexibility,a novel sensor scheduling algorithm based on the deep reinforcement learning is proposed.Firstly,the sensors’co-scheduling strategy in UWSNs is formulated as Markov decision process(MDP).

A strategy resisting wrinkling of sandwich structures reinforced using functionally-graded carbon nanotubes

Sandwich structures have been widely applied in the wing and the horizontal tail of the aircraft,so face sheets of such structure might occur wrinkling deformation in the process of service,which will largely decrease capability of sustaining loads.As a result,this paper aims at proposing a reasonable strategy resisting wrinkling deformation of sandwich structures.To this end,an enhanced higher-order model has been proposed for wrinkling analysis of sandwich structures.Buckling behaviors of a five-layer sandwich plate are firstly analyzed,which is utilized to assess performance of the proposed model.Subsequently,wrinkling behaviors of four sandwich plates are further investigated by utilizing present model,which have been evaluated by using quasi threedimensional(3D)elasticity solutions,3D Finite Element Method(3D-FEM)results and experimental datum.Finally,the present model is utilized to study the buckling and the wrinkling behaviors of sandwich plates reinforced by Carbon Nano Tubes(CNTs).In addition,influence of distribution profile of CNTs on wrinkling behaviors has been analyzed,and a typical distribution profile of CNTs has been chosen to resist wrinkling deformation.Without increase of additional weight,the present strategy can effectively resist wrinkling deformation of sandwich plates,which is rarely reported in published literature.

A graph-based two-stage classification network for mobile screen defect inspection

Defect inspection,also known as defect detection,is significant in mobile screen quality control.There are some challenging issues brought by the characteristics of screen defects,including the following:(1)the problem of interclass similarity and intraclass variation,(2)the difficulty in distinguishing low contrast,tiny-sized,or incomplete defects,and(3)the modeling of category dependencies for multi-label images.To solve these problems,a graph reasoning module,stacked on a classification module,is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency,image-wise relations,and interactions between them.To further improve the classification performance,the classifier of the classification module is redesigned as a cosine similarity function.With the help of contrastive learning,the classification module can better initialize the category-wise graph of the reasoning module.Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances:97.7%accuracy and 97.3%F-measure.This proves that the proposed approach is effective in industrial applications.

Multi-agent evaluation for energy management by practically scalingα-rank

Currently,decarbonization has become an emerging trend in the power system arena.However,the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues,providing challenges for voltage regulation across a large-scale power grid network.Reinforcement learning based intelligent control of smart inverters and other smart building energy management(EM)systems can be leveraged to alleviate these issues.To achieve the best EM strategy for building microgrids in a power system,this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’comfort while pursuing systemlevel objectives.The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels.Theα-rank algorithm can solve the general-sum game and guarantee the ranking theoretically,but it is limited by the interaction complexity and hardly applies to the practical power system.A new evaluation algorithm(TcEval)is proposed by practically scaling theα-rank algorithm through a tensor complement to reduce the interaction complexity.Then,considering the noise prevalent in practice,a noise processing model with domain knowledge is built to calculate the strategy payoffs,and thus the TcEval-AS algorithm is proposed when noise exists.Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches,including ResponseGraphUCB(RG-UCB)andαInformationGain(α-IG).Finally,the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

Robust global route planning for an autonomous underwater vehicle in a stochastic environment

This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic.The problem is formulated as a variant of the orienteering problem.Based on the genetic algorithm(GA),we propose the greedy strategy based GA(GGA)which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization,and use a differential evolution planner for providing the deterministic local path cost.The uncertainty of the local path cost comes from unpredictable obstacles,measurement error,and trajectory tracking error.To improve the robustness of the planner in an uncertain environment,a sampling strategy for path evaluation is designed,and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths.Monte Carlo simulations are used to verify the superiority and effectiveness of the planner.The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6%in terms of total profit,and the sampling-based GGA route planner(S-GGARP)improves the average profit by 5.5%compared to the GGA route planner(GGARP).

Cloud-assisted cognition adaptation for service robots in changing home environments

Robots need more intelligence to complete cognitive tasks in home environments.In this paper,we present a new cloud-assisted cognition adaptation mechanism for home service robots,which learns new knowledge from other robots.In this mechanism,a change detection approach is implemented in the robot to detect changes in the user’s home environment and trigger the adaptation procedure that adapts the robot’s local customized model to the environmental changes,while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion.First,three different model fusion methods are proposed to carry out the adaptation procedure,and two key factors of the fusion methods are emphasized.Second,the most suitable model fusion method and its settings for the cloud–robot knowledge transfer are determined.Third,we carry out a case study of learning in a changing home environment,and the experimental results verify the efficiency and effectiveness of our solutions.The experimental results lead us to propose an empirical guideline of model fusion for the cloud–robot knowledge transfer.