The binocular stereo vision is the lowest cost sensor for obtaining 3D information.Considering the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urg...The binocular stereo vision is the lowest cost sensor for obtaining 3D information.Considering the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture.To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this paper.Simulating the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy improving.Inspired by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable perception.In various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity accuracy.For fixed distance measurement,the maximum improvement was 78.6%by standard deviation.Based on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and quantity.The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.展开更多
Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions ...Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions are mainly three folds:first,a frame-work combining imitation learning with deep reinforcement learning is presented,which enables a robot to learn a stable navigation policy faster in the target-driven navigation task.Second,the surrounding images is taken as the observation instead of sequential images,which can improve the navigation performance for more information.Moreover,a simple yet efficient template matching method is adopted to determine the stop action,making the system more practical.Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches,which demonstrate the effectiveness and efficiency of our approach.展开更多
The problem of finding the minimum spanning tree(MST)is one of the most studied and important combinatorial optimisation problems in graph theory.Several types of uncertainties exist in real-life problems,which make i...The problem of finding the minimum spanning tree(MST)is one of the most studied and important combinatorial optimisation problems in graph theory.Several types of uncertainties exist in real-life problems,which make it very hard to find the exact length of the arc.The neutrosophic set is an efficient tool to model and deal with the uncertainties in information due to inconsistent and indeterminate.In this study,the authors use triangular neutrosophic numbers to represent the edge weights of a neutrosophic graph for the MST problem in the neutrosophic environment.They call this problem a neutrosophic MST(NMST)problem.They formulate the NMST problem in terms of the linear programming model.Here,they introduce an algorithmic method based on a genetic algorithm for solving the NMST problem.They present the utility of triangular neutrosophic numbers as edge weights and their application in the electrical distribution network.展开更多
Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and in-heritance.However,previous aesthetic evaluation studies often focussed on photos and paintings,with few attempts on Chinese callig...Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and in-heritance.However,previous aesthetic evaluation studies often focussed on photos and paintings,with few attempts on Chinese calligraphy.To solve this problem,a Siamese regression aesthetic fusion method is proposed,named SRAFE,for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning.First,a dataset termed Evaluated Chinese Calligraphy Copies(E3C)is constructed for aesthetic evalu-ation.Second,12 hand‐crafted aesthetic features based on the shape,structure,and stroke of calligraphy are designed.Then,the Siamese regression network(SRN)is designed to extract the deep aesthetic representation of calligraphy.Finally,the SRAFE method is built by fusing the deep aesthetic features with the hand‐crafted aesthetic features.Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C,proving the effectiveness of the authors’method.展开更多
With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task...With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods.For the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep time.The complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and classification.In this study,we have extracted 12 statistical properties from input signals.The proposed models are tested in three different combinations of features sets.In the first experiment,the feature set contained all the 12 features.The second and third experiments were conducted with the nine and five best features.The patient records come from the ISRUC-Sleep database.The highest classification accuracy was achieved for sleep staging through combinations with the five feature set.From the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.展开更多
Classical radial basis function network(RBFN)is widely used to process the non-linear separable data sets with the introduction of activation functions.However,the setting of parameters for activation functions is ran...Classical radial basis function network(RBFN)is widely used to process the non-linear separable data sets with the introduction of activation functions.However,the setting of parameters for activation functions is random and the distribution of patterns is not taken into account.To process this issue,some scholars introduce the kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions.On the base of the original kernel clustering,this study further discusses the influence of kernel clustering on an RBFN when the setting of kernel clustering is changing.The changing involves different kernel-clustering ways[bubble sort(BS)and escape nearest outlier(ENO)],multiple kernel-clustering criteria(static and dynamic)etc.Experimental results validate that with the consideration of distribution of patterns and the changes of setting of kernel clustering,the performance of an RBFN is improved and is more feasible for corresponding data sets.Moreover,though BS always costs more time than ENO,it still brings more feasible clustering results.Furthermore,dynamic criterion always cost much more time than static one,but kernel number derived from dynamic criterion is fewer than the one from static.展开更多
High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the ...High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the effect.To improve the precision of lane keeping,this study presents a novel multi-state model-based end-to-end lane keeping method.First,three driving states will be defined:going straight,turning right and turning left.Second,the finite-state machine(FSM)table as well as three kinds of training datasets will be generated based on the three driving states.Instead of collecting the dataset by human drivers,the accurate dataset will be collected by the high-performance path following controller.Third,three sets of parameters based on 3DCNN-LSTM model will be trained for going straight,turning left and turning right,which will be combined with FSM table to form a multi-state model.This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error.The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.展开更多
Robot calligraphy visually reflects the motion capability of robotic manipulators.While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article...Robot calligraphy visually reflects the motion capability of robotic manipulators.While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works.The key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data hierarchically.In this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module.The robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with GANs.Then the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is evaluated.Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.展开更多
It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions.The multigranulation decision theory based on conditional probability and cost l...It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions.The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision-making problems from multi-levels and multi-angles,and the neighbourhood rough set model(NRS)can facilitate the analysis and processing of numerical or mixed type data,and can address the limitation of multigranulation decision-theoretic rough sets(MG-DTRS),which is not easy to cope with complex data.Based on the in-depth study of hybrid-valued decision systems and MG-DTRS models,this study analysed neigh-bourhood MG-DTRS(NMG-DTRS)deeply by fusing MG-DTRS and NRS;a matrix-based approach for approximation sets of NMG-DTRS model was proposed on the basis of the matrix representations of concepts;the positive,boundary and negative domains were constructed from the matrix perspective,and the concept of positive decision recognition rate was introduced.Furthermore,the authors explored the related properties of NMG-DTRS model,and designed and described the corresponding solving algorithms in detail.Finally,some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm,but also showed the relationship between the decision recognition rate and the granularity and threshold.展开更多
In this study,the authors envisage the neutrosophic number from various distinct rational perspectives and viewpoints to give it a look of a conundrum.They focused and analysed neutrosophic fuzzy numbers when indeterm...In this study,the authors envisage the neutrosophic number from various distinct rational perspectives and viewpoints to give it a look of a conundrum.They focused and analysed neutrosophic fuzzy numbers when indeterminacy and falsity functions are dependent on each other,which serves an indispensable role for the uncertainty concept.Additionally,the idea of cylindrical neutrosophic single-valued number is focused here,when the indeterminacy and falsity functions are dependent to each other using an influx of different logical and innovative graphical representation.They also developed the score and accuracy function for this particular cylindrical neutrosophic single-valued number and analysed some real-life problems like networking critical path model problem and minimal spanning tree problem of operation research field when the numbers are in cylindrical neutrosophic ambiance.They also introduced a multi-criterion group decision-making problem in this cylindrical neutrosophic domain.This noble thought will help us to solve a plethora of daily life problems in the neutrosophic arena.展开更多
This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the disc...This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the discrete firefly algorithm (DFFA) for the formation and implementation of makespan minimisation on parallel machines, followed by performing Ctrie-based caching for pairwise alignment to reduce the load on the data servers for handling multiple queries. The proposed strategy addresses a multi-client problem that aims to acquire the full advantage of the computational power of parallel connected machines. Further, it is shown that the inclusion of Ctrie as caching mechanism successively improves the performance of the system with accretion in several sequences. Performance of proposed DFFA is also compared with discrete versions of four swarm intelligence based algorithms at the criteria of makespan minimisation and the rate of convergence on two kinds of complex and diverse datasets. The work is unique in this sense: it is the first swarm-intelligence-based implementation for the addressed problem;it is so far the first approach for Ctrie based caching of the MSA on the scheduled parallel machines;hybridisation of DFFA with Ctrie for caching the MSA results is also a novel implementation.展开更多
Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)netwo...Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.展开更多
In order to deal with coarse-grained and multi-grained calculation problems,as well as granularity transformation problems in information system,quotient space theory is introduced in rough set theory.The main idea of...In order to deal with coarse-grained and multi-grained calculation problems,as well as granularity transformation problems in information system,quotient space theory is introduced in rough set theory.The main idea of this research is to try to maintain the important properties of the original space into the quotient space.Aimed to preserve the micro properties and the macro properties,two pairs of approximation operators on the quotient space are defined.When it comes to the composite of quotient spaces,the idea of these operators shows greater advantages.Examples are cited to illustrate possible applications of these operators,and their matrix representations are also given to make the calculations easy.Finally,all approximation operators on the quotient space involved so far are compared and their relationships are shown through a diagram.展开更多
Traceability system is one of the popular applications of graphic blockchains.However,there are centralisation problems and a long time for final consistency confirmation in the graphic blockchain.In addition,the bloc...Traceability system is one of the popular applications of graphic blockchains.However,there are centralisation problems and a long time for final consistency confirmation in the graphic blockchain.In addition,the blockchain system in the traceability application scenario has the problem of insufficient supervision.Therefore,a witness-based graphic blockchain consensus mechanism is proposed.In the consensus mechanism,a verifiable random function is used to screen the publishers of the unit;an SM2 threshold signature is used to sign the unit information to improve the non-repudiation of the traceability information uploaders to the unit information under the supervision of the witness.The improved consistency algorithm cancels the process of finding a stable main chain and makes relatively many nodes to participate in the consensus process.The experimental results show that the graphic blockchain using the improved witness mechanism can reduce the degree of centralisation,shorten the time for new units to reach consensus,and greatly ensure the security and scalability of the blockchain system.展开更多
Media Convergence is the merging of mass communication outlets—print,television,radio,and the Internet—along with portable and interactive technologies through various digital media platforms.As a new influential ma...Media Convergence is the merging of mass communication outlets—print,television,radio,and the Internet—along with portable and interactive technologies through various digital media platforms.As a new influential mainstream media,Media Convergence has now become a national strategy to integrate multiple media forms into one platform.Ideally,the intelligent media computing technology and application,including 5G,Augmented Reality/Visual Reality,Natural Language Processing,Computer Vision,Robotics,Big data,and Machine/Deep/Reinforcement/Transfer learning,should evolve into a knowledge base for purposes of delivering a dynamic experience and innovating media communication methods.However,how does one integrated media provide effective algorithm structures and tools that could merge,transform,and process various media forms,that is,crossmodal/multi-modal learning and representation?This question remains to be answered.展开更多
基金funded by the National Natural Science Foundation of China(No.51979275)Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Nat-ural Resources in Megacities,MNR(No.KFKT‐2022‐05)+3 种基金Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(No.KF‐2021‐06‐115)Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems,Bei-hang University(No.VRLAB2022C10)Jiangsu Province and Education Ministry Co‐sponsored Synergistic Innovation Center of Modern Agricultural Equipment(No.XTCX2002)2115 Talent Development Program of China Agricultural University and Chinese Universities Scientific Fund(No.2021TC105).
文摘The binocular stereo vision is the lowest cost sensor for obtaining 3D information.Considering the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture.To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this paper.Simulating the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy improving.Inspired by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable perception.In various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity accuracy.For fixed distance measurement,the maximum improvement was 78.6%by standard deviation.Based on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and quantity.The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.
基金National Natural Science Foundation of China,Grant/Award Numbers:61703418,61825305。
文摘Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions are mainly three folds:first,a frame-work combining imitation learning with deep reinforcement learning is presented,which enables a robot to learn a stable navigation policy faster in the target-driven navigation task.Second,the surrounding images is taken as the observation instead of sequential images,which can improve the navigation performance for more information.Moreover,a simple yet efficient template matching method is adopted to determine the stop action,making the system more practical.Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches,which demonstrate the effectiveness and efficiency of our approach.
基金This project was supported by the National Natural Science Foundation of China Research on the Precision Evaluation Model of Goaf Pressure Relief Gas Drainage Based on LSTM Regression no.(51804248)Science and Technology Project of State Grid Zizang Electric Power Co.,Ltd(SGXZJY00JHJS2000008)Research Technology Service of Multi Energy Complimentary Demonstration Application。
文摘The problem of finding the minimum spanning tree(MST)is one of the most studied and important combinatorial optimisation problems in graph theory.Several types of uncertainties exist in real-life problems,which make it very hard to find the exact length of the arc.The neutrosophic set is an efficient tool to model and deal with the uncertainties in information due to inconsistent and indeterminate.In this study,the authors use triangular neutrosophic numbers to represent the edge weights of a neutrosophic graph for the MST problem in the neutrosophic environment.They call this problem a neutrosophic MST(NMST)problem.They formulate the NMST problem in terms of the linear programming model.Here,they introduce an algorithmic method based on a genetic algorithm for solving the NMST problem.They present the utility of triangular neutrosophic numbers as edge weights and their application in the electrical distribution network.
基金This work is supported by National Natural Science Foundation of China （NSFC, No. 61340046）, National High Technology Research and Development Program of China （863 Program, No. 2006AA04Z247）, Scientific and Technical Innovation Commission of Shenzhen Municipality （JCYJ20130331144631730, JCYJ20130331144716089）, Specialized Research Fund for the Doctoral Program of Higher Education （No. 20130001110011）.
文摘Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and in-heritance.However,previous aesthetic evaluation studies often focussed on photos and paintings,with few attempts on Chinese calligraphy.To solve this problem,a Siamese regression aesthetic fusion method is proposed,named SRAFE,for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning.First,a dataset termed Evaluated Chinese Calligraphy Copies(E3C)is constructed for aesthetic evalu-ation.Second,12 hand‐crafted aesthetic features based on the shape,structure,and stroke of calligraphy are designed.Then,the Siamese regression network(SRN)is designed to extract the deep aesthetic representation of calligraphy.Finally,the SRAFE method is built by fusing the deep aesthetic features with the hand‐crafted aesthetic features.Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C,proving the effectiveness of the authors’method.
文摘With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods.For the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep time.The complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and classification.In this study,we have extracted 12 statistical properties from input signals.The proposed models are tested in three different combinations of features sets.In the first experiment,the feature set contained all the 12 features.The second and third experiments were conducted with the nine and five best features.The patient records come from the ISRUC-Sleep database.The highest classification accuracy was achieved for sleep staging through combinations with the five feature set.From the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.
基金This work was sponsored by the‘Chenguang Program’supported by the Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant no.18CG54.Furthermore,this work was also supported by the National Natural Science Foundation of China(CN)under Grant nos.61602296 and 61673301the Natural Science Foundation of Shanghai(CN)under Grant no.16ZR1414500+1 种基金Project funded by the China Postdoctoral Science Foundation under Grant no.2019M651576the National Key R&D Program of China(Grant no.213).
文摘Classical radial basis function network(RBFN)is widely used to process the non-linear separable data sets with the introduction of activation functions.However,the setting of parameters for activation functions is random and the distribution of patterns is not taken into account.To process this issue,some scholars introduce the kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions.On the base of the original kernel clustering,this study further discusses the influence of kernel clustering on an RBFN when the setting of kernel clustering is changing.The changing involves different kernel-clustering ways[bubble sort(BS)and escape nearest outlier(ENO)],multiple kernel-clustering criteria(static and dynamic)etc.Experimental results validate that with the consideration of distribution of patterns and the changes of setting of kernel clustering,the performance of an RBFN is improved and is more feasible for corresponding data sets.Moreover,though BS always costs more time than ENO,it still brings more feasible clustering results.Furthermore,dynamic criterion always cost much more time than static one,but kernel number derived from dynamic criterion is fewer than the one from static.
基金National Natural Science Foundation of China(U1764264/61873165).
文摘High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the effect.To improve the precision of lane keeping,this study presents a novel multi-state model-based end-to-end lane keeping method.First,three driving states will be defined:going straight,turning right and turning left.Second,the finite-state machine(FSM)table as well as three kinds of training datasets will be generated based on the three driving states.Instead of collecting the dataset by human drivers,the accurate dataset will be collected by the high-performance path following controller.Third,three sets of parameters based on 3DCNN-LSTM model will be trained for going straight,turning left and turning right,which will be combined with FSM table to form a multi-state model.This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error.The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.
基金National Key Research and Development Program of China,Grant/Award Numbers:2021YFB2501301,2019YFB1600704The Science and Technology Development Fund,Grant/Award Numbers:0068/2020/AGJ,SKL‐IOTSC(UM)‐2021‐2023GDST,Grant/Award Numbers:2020B1212030003,MYRG2022‐00192‐FST。
文摘Robot calligraphy visually reflects the motion capability of robotic manipulators.While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works.The key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data hierarchically.In this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module.The robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with GANs.Then the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is evaluated.Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.
基金the Universities Natural Science Key Project of Anhui Province,Grant/Award Number:KJ2020A0637。
文摘It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions.The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision-making problems from multi-levels and multi-angles,and the neighbourhood rough set model(NRS)can facilitate the analysis and processing of numerical or mixed type data,and can address the limitation of multigranulation decision-theoretic rough sets(MG-DTRS),which is not easy to cope with complex data.Based on the in-depth study of hybrid-valued decision systems and MG-DTRS models,this study analysed neigh-bourhood MG-DTRS(NMG-DTRS)deeply by fusing MG-DTRS and NRS;a matrix-based approach for approximation sets of NMG-DTRS model was proposed on the basis of the matrix representations of concepts;the positive,boundary and negative domains were constructed from the matrix perspective,and the concept of positive decision recognition rate was introduced.Furthermore,the authors explored the related properties of NMG-DTRS model,and designed and described the corresponding solving algorithms in detail.Finally,some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm,but also showed the relationship between the decision recognition rate and the granularity and threshold.
文摘In this study,the authors envisage the neutrosophic number from various distinct rational perspectives and viewpoints to give it a look of a conundrum.They focused and analysed neutrosophic fuzzy numbers when indeterminacy and falsity functions are dependent on each other,which serves an indispensable role for the uncertainty concept.Additionally,the idea of cylindrical neutrosophic single-valued number is focused here,when the indeterminacy and falsity functions are dependent to each other using an influx of different logical and innovative graphical representation.They also developed the score and accuracy function for this particular cylindrical neutrosophic single-valued number and analysed some real-life problems like networking critical path model problem and minimal spanning tree problem of operation research field when the numbers are in cylindrical neutrosophic ambiance.They also introduced a multi-criterion group decision-making problem in this cylindrical neutrosophic domain.This noble thought will help us to solve a plethora of daily life problems in the neutrosophic arena.
文摘This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the discrete firefly algorithm (DFFA) for the formation and implementation of makespan minimisation on parallel machines, followed by performing Ctrie-based caching for pairwise alignment to reduce the load on the data servers for handling multiple queries. The proposed strategy addresses a multi-client problem that aims to acquire the full advantage of the computational power of parallel connected machines. Further, it is shown that the inclusion of Ctrie as caching mechanism successively improves the performance of the system with accretion in several sequences. Performance of proposed DFFA is also compared with discrete versions of four swarm intelligence based algorithms at the criteria of makespan minimisation and the rate of convergence on two kinds of complex and diverse datasets. The work is unique in this sense: it is the first swarm-intelligence-based implementation for the addressed problem;it is so far the first approach for Ctrie based caching of the MSA on the scheduled parallel machines;hybridisation of DFFA with Ctrie for caching the MSA results is also a novel implementation.
基金This work is partially supported by National Basic Research Priorities Programme （No. 2013CB329502）, Na-tional Natural Science Foundation of China （No. 61472468, 61502115）, General Research Fund of Hong Kong （No. 417112）, and Fundamental Research Funds for the Central Universities （No. 3262014T75, 3262015T20, 3262015T70, 3262016T31）.
基金supported by the project SP2023/009“Development of algorithms and systems for control,mea-surement and safety applications IX”of the Student Grant System,VSB‐TU Ostrava.This work was also supproted by the project FW03010194“Development of a System for Monitoring and Evaluation of Selected Risk Factors of Physical Workload in the Context of Industry 4.0″of the Technology Agency of the Czech Republicfunding from the European Union's Horizon 2020 research and innovation programme under grant agreement No.856670.This research received no external funding.
文摘Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.
基金supported by the National Natural Science Foundation of China under Grant 61672107funded by the China Scholarship Council(201606475016).
文摘In order to deal with coarse-grained and multi-grained calculation problems,as well as granularity transformation problems in information system,quotient space theory is introduced in rough set theory.The main idea of this research is to try to maintain the important properties of the original space into the quotient space.Aimed to preserve the micro properties and the macro properties,two pairs of approximation operators on the quotient space are defined.When it comes to the composite of quotient spaces,the idea of these operators shows greater advantages.Examples are cited to illustrate possible applications of these operators,and their matrix representations are also given to make the calculations easy.Finally,all approximation operators on the quotient space involved so far are compared and their relationships are shown through a diagram.
文摘Traceability system is one of the popular applications of graphic blockchains.However,there are centralisation problems and a long time for final consistency confirmation in the graphic blockchain.In addition,the blockchain system in the traceability application scenario has the problem of insufficient supervision.Therefore,a witness-based graphic blockchain consensus mechanism is proposed.In the consensus mechanism,a verifiable random function is used to screen the publishers of the unit;an SM2 threshold signature is used to sign the unit information to improve the non-repudiation of the traceability information uploaders to the unit information under the supervision of the witness.The improved consistency algorithm cancels the process of finding a stable main chain and makes relatively many nodes to participate in the consensus process.The experimental results show that the graphic blockchain using the improved witness mechanism can reduce the degree of centralisation,shorten the time for new units to reach consensus,and greatly ensure the security and scalability of the blockchain system.
文摘Media Convergence is the merging of mass communication outlets—print,television,radio,and the Internet—along with portable and interactive technologies through various digital media platforms.As a new influential mainstream media,Media Convergence has now become a national strategy to integrate multiple media forms into one platform.Ideally,the intelligent media computing technology and application,including 5G,Augmented Reality/Visual Reality,Natural Language Processing,Computer Vision,Robotics,Big data,and Machine/Deep/Reinforcement/Transfer learning,should evolve into a knowledge base for purposes of delivering a dynamic experience and innovating media communication methods.However,how does one integrated media provide effective algorithm structures and tools that could merge,transform,and process various media forms,that is,crossmodal/multi-modal learning and representation?This question remains to be answered.
