The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most ...Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most promising directions of development.This paper proposes an optimized schedulingmodel for a hydrogen-coupled electro-heat-gas integrated energy system(HCEHG-IES)using generative adversarial imitation learning(GAIL).The model aims to enhance renewable-energy absorption,reduce carbon emissions,and improve grid-regulation flexibility.First,the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process(MDP).To overcome the limitations of conventional deep reinforcement learning algorithms—including long optimization time,slow convergence,and subjective reward design—this study augments the PPO algorithm by incorporating a discriminator network and expert data.The newly developed algorithm,termed GAIL,enables the agent to perform imitation learning from expert data.Based on this model,dynamic scheduling decisions are made in continuous state and action spaces,generating optimal energy-allocation and management schemes.Simulation results indicate that,compared with traditional reinforcement-learning algorithms,the proposed algorithmoffers better economic performance.Guided by expert data,the agent avoids blind optimization,shortens the offline training time,and improves convergence performance.In the online phase,the algorithm enables flexible energy utilization,thereby promoting renewable-energy absorption and reducing carbon emissions.展开更多
This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The partici...This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.展开更多
Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high c...Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.展开更多
Aim The particle texture from diesel engine was imitated by use of computer. Methods The theory of fractal geometry and the diffusion limited aggregation model were used to simulate the micron texture. Results The...Aim The particle texture from diesel engine was imitated by use of computer. Methods The theory of fractal geometry and the diffusion limited aggregation model were used to simulate the micron texture. Results The fractal dimensions of granule distribution and corpuscle superficial area are quite conformed with those of measurement. Conclusion The texture parameters of engine particle cluster can be obtained precisely by use of fractal theory.展开更多
UG and imitation are two parallel hypotheses trying to answer how childrens language acquisition is realized. Imitation fails to explain how children acquire language; however, it helps a lot in childrens language acq...UG and imitation are two parallel hypotheses trying to answer how childrens language acquisition is realized. Imitation fails to explain how children acquire language; however, it helps a lot in childrens language acquisition.展开更多
One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the envir...One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the environment, and it is possible for an individual to use two or more rules to update their strategy. We consider the case where an individual updates strategies based on the Moran and imitation processes, and establish mixed stochastic evolutionary game dynamics by combining both processes. Our aim is to study how individuals change strategies based on two update rules and how this affects evolutionary game dynamics. We obtain an analytic expression and properties of the fixation probability and fixation times(the unconditional fixation time or conditional average fixation time) associated with our proposed process. We find unexpected results. The fixation probability within the proposed model is independent of the probabilities that the individual adopts the imitation rule update strategy. This implies that the fixation probability within the proposed model is equal to that from the Moran and imitation processes. The one-third rule holds in the proposed mixed model. However, under weak selection, the fixation times are different from those of the Moran and imitation processes because it is connected with the probability that individuals adopt an imitation update rule. Numerical examples are presented to illustrate the relationships between fixation times and the probability that an individual adopts the imitation update rule, as well as between fixation times and selection intensity. From the simulated analysis, we find that the fixation time for a mixed process is greater than that of the Moran process, but is less than that of the imitation process. Moreover, the fixation times for a cooperator in the proposed process increase as the probability of adopting an imitation update increases; however, the relationship becomes more complex than a linear relationship.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
Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and th...Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.展开更多
Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly acc...Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.展开更多
This paper reports on a study on the effects of reading-writing integrated tasks on vocabulary learning and explored the differential roles of creative construction and non-creative construction in promoting lexical l...This paper reports on a study on the effects of reading-writing integrated tasks on vocabulary learning and explored the differential roles of creative construction and non-creative construction in promoting lexical learning. Participants were 90 first-year English majors, randomly assigned to two experimental groups(continuation and retelling) and one control group, with 30 students in each group. Results showed that the continuation group generated a substantial amount of creative construction and produced significantly more instances of creative imitation than the retelling group. The continuation group outperformed the retelling group for both receptive and productive vocabulary knowledge gain and retention, but differences were only significant in terms of productive vocabulary retention. Finally, productive vocabulary knowledge retention among the continuation group was significantly and positively correlated with creative imitation(meaning creation coupled with language imitation), but not with linguistic alignment per se. As productive vocabulary knowledge constitutes the learner ’s ability to use lexical knowledge to express ideas in dynamic contexts, the findings afforded evidence that creative imitation could be the answer to the fundamental issue of L2 learning(i.e., mapping static language onto dynamic idea expression). The pedagogical implications as well as future research directions are also discussed.展开更多
Environmental comprehensive management system, called “the bionic community”, can be established in imitation of biome, which can transform the wastes generated in a certain field into the raw materials of other fie...Environmental comprehensive management system, called “the bionic community”, can be established in imitation of biome, which can transform the wastes generated in a certain field into the raw materials of other field. The establishment of the bionic community includes two aspects, i.e., the matching technique and the management system. The main matching technique is the preparation of composite materials made of various wastes. This new kind of material can be divided into four types: polymer matrix, silicate matrix, metal matrix and carbon matrix(or ceramic matrix). The environmental comprehensive management system is formed by organizing a trans-trades joint-management business entity with the products of composite material made of wastes at the core.展开更多
The theory of“imitation”in painting occupies a leading position in western art,which originated from the theory of“imitation”in ancient Greece,and has become one of the art theories affecting the world through the...The theory of“imitation”in painting occupies a leading position in western art,which originated from the theory of“imitation”in ancient Greece,and has become one of the art theories affecting the world through the continuous development of later generations.Through the exploration of the source of“imitation”in China and the West,there are some comments on the meaning of“imitation”in Chinese classical painting theory,such as“transfer model writing”and“image form”,which is obvious differences from the west.Traditional Chinese painting is a combination of careful observation of natural things and subjective emotions to express their own aesthetic feelings,and ultimately form a vivid artistic conception.Modern imitation is borrowed from Western imitation.In fact,imitation in traditional painting has its own meaning,which contains Chinese aesthetic thought.“Imitation”aesthetics is unique in traditional Chinese painting and is the most important form of painting art.展开更多
This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics.We propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to fi...This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics.We propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control inputs.This allows us to address the imitation learning problem without prior knowledge of the expert's system dynamics.To achieve this,we provide a basic model-based algorithm that is built upon RL and inverse optimal control.This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators.Theoretical analysis and simulation examples verify the methods.展开更多
Gesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms. Among the numerous applications are physical rehabilitation and imitation games. In this work, we...Gesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms. Among the numerous applications are physical rehabilitation and imitation games. In this work, we suggest performing human gesture recognition within the context of a serious imitation game, which would aim at improving social interactions with teenagers with autism spectrum disorders. We use an artificial intelligence algorithm to detect the skeleton of the participant, then model the human pose space and describe an imitation learning method using a Gaussian Mixture Model in the Riemannian manifold.展开更多
When discussing the roots of Arab theatre, we find ourselves confronting two main streams of thought. The first one, represented by prominent Arab writers like Najib Mahfuz, Abbas A1-Aqqad, M. Badawi, and other critic...When discussing the roots of Arab theatre, we find ourselves confronting two main streams of thought. The first one, represented by prominent Arab writers like Najib Mahfuz, Abbas A1-Aqqad, M. Badawi, and other critics, rejects the theory that an Arab theatre existed before the mid-19th century. The second stream, represented by prominent scholars like Ali A1-Rai, Ibrahim Hamada and S. Moreh, see modern Arab theatre as part of a continuum, emphasizing of some elements of dramatic manifestations in Arab literary heritage. This paper intends to examine these two streams, their evidences and arguments. Such examination will shed some light on the origin of Arab theatre as a literary genre, and how it was influenced, if any, by Western theatrical heritage. Thus, answering the main question of this paper, whether Arab theatre is original or simply a Western imitation.展开更多
This paper describes parody as an effective teaching and learning practice in Advanced English course offered to English major juniors and seniors. The objective of this course is to help heighten students towards a m...This paper describes parody as an effective teaching and learning practice in Advanced English course offered to English major juniors and seniors. The objective of this course is to help heighten students towards a more advanced level of English proficiency, and its main preoccupation is doing intensive analyses of carefully selected texts which amount to well-established classics and are characterized with linguistic complexity. To enhance students＇ learning, we embed in the course a practice of parody, which here refers to the creation of an imitative work of an original written work, usually with an attendant comic effect. Upon the completion of each module, students are assigned the task of parodying part of the text, which involves recasting its overall content while retaining formal framework, thus offering students a means of re-paying homage to the excellence of the text. Writing a parody demands great artistry in shaping a creatively simulative work, in fitting exotic content into a local form, and in transplanting new experiential logic into old textual order. The parodies are then peer-reviewed as well as instructor-reviewed. Close observation and survey show that the students have displayed heightened motivation in engaging themselves in the practice and they have benefited greatly from it. Parody proves a particularly fruitful technique in teaching and learning Advanced English, and may also be useful in teaching English writing, since it entitles students to a very unique mode of savoring and wielding the artistic power of the English language.展开更多
The imitation cheese is the product with the cheese characteristics which can meet the specific requirements by using the non-milk-derived protein and fat or the full substitution of the milk protein and the milk fat....The imitation cheese is the product with the cheese characteristics which can meet the specific requirements by using the non-milk-derived protein and fat or the full substitution of the milk protein and the milk fat.This paper introduces the production principles,processes and key control points of the imitation cheese with the soybean as main raw material,summarizes the researches and development of the soybean imitation cheese in recent years,and forecasts the market prospect of the soybean imitation cheese in China.展开更多
During the translation theoretically developments, the discussion on the standards of translation have never been stopped in term of different times, different countries, by different scholars and translators. Some th...During the translation theoretically developments, the discussion on the standards of translation have never been stopped in term of different times, different countries, by different scholars and translators. Some theories share the same or similar superficies and theoretical representation, but they are totally different at a deeper level. However, without restricting from language and culture, time and space, they are interlinked inside. It is interrogative to produce a consolidation with subtle differences of theories. This paper introduces and analyzes the ＇imitation＇ and ＇distortion＇ on the bases of the Three Types of Translation of John Dryden and Translation Theory of Mao dun; by comparing their theory to provide references to the further translation study.展开更多
Imitation models for computing the environmental water pollution level depending on the intensity of pollution sources created by the author over the years are presented. For this purpose, an additive model of a non-s...Imitation models for computing the environmental water pollution level depending on the intensity of pollution sources created by the author over the years are presented. For this purpose, an additive model of a non-stationary random process is considered. For the modeling of its components, models that consider only dilution and self-purification processes are proposed for waste water and three-dimensional turbulent diffusion equations for river waters, and multidimensional Gaussian Markov series are proposed for modeling the random component. The purpose, the capabilities and the peculiarities of such imitation models are discussed taking into account the peculiarities of the water objects. The modular principle of creating imitation models is proposed to facilitate their development and use.展开更多
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金supported by State Grid Corporation Technology Project(No.522437250003).
文摘Hydrogen energy is a crucial support for China’s low-carbon energy transition.With the large-scale integration of renewable energy,the combination of hydrogen and integrated energy systems has become one of the most promising directions of development.This paper proposes an optimized schedulingmodel for a hydrogen-coupled electro-heat-gas integrated energy system(HCEHG-IES)using generative adversarial imitation learning(GAIL).The model aims to enhance renewable-energy absorption,reduce carbon emissions,and improve grid-regulation flexibility.First,the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process(MDP).To overcome the limitations of conventional deep reinforcement learning algorithms—including long optimization time,slow convergence,and subjective reward design—this study augments the PPO algorithm by incorporating a discriminator network and expert data.The newly developed algorithm,termed GAIL,enables the agent to perform imitation learning from expert data.Based on this model,dynamic scheduling decisions are made in continuous state and action spaces,generating optimal energy-allocation and management schemes.Simulation results indicate that,compared with traditional reinforcement-learning algorithms,the proposed algorithmoffers better economic performance.Guided by expert data,the agent avoids blind optimization,shortens the offline training time,and improves convergence performance.In the online phase,the algorithm enables flexible energy utilization,thereby promoting renewable-energy absorption and reducing carbon emissions.
文摘This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.
基金Supported by National Natural Science Foundation of China(Grant No.52475280)Shaanxi Provincial Natural Science Basic Research Program(Grant No.2025SYSSYSZD-105).
文摘Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.
文摘Aim The particle texture from diesel engine was imitated by use of computer. Methods The theory of fractal geometry and the diffusion limited aggregation model were used to simulate the micron texture. Results The fractal dimensions of granule distribution and corpuscle superficial area are quite conformed with those of measurement. Conclusion The texture parameters of engine particle cluster can be obtained precisely by use of fractal theory.
文摘UG and imitation are two parallel hypotheses trying to answer how childrens language acquisition is realized. Imitation fails to explain how children acquire language; however, it helps a lot in childrens language acquisition.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71871171,71871173,and 71832010)
文摘One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the environment, and it is possible for an individual to use two or more rules to update their strategy. We consider the case where an individual updates strategies based on the Moran and imitation processes, and establish mixed stochastic evolutionary game dynamics by combining both processes. Our aim is to study how individuals change strategies based on two update rules and how this affects evolutionary game dynamics. We obtain an analytic expression and properties of the fixation probability and fixation times(the unconditional fixation time or conditional average fixation time) associated with our proposed process. We find unexpected results. The fixation probability within the proposed model is independent of the probabilities that the individual adopts the imitation rule update strategy. This implies that the fixation probability within the proposed model is equal to that from the Moran and imitation processes. The one-third rule holds in the proposed mixed model. However, under weak selection, the fixation times are different from those of the Moran and imitation processes because it is connected with the probability that individuals adopt an imitation update rule. Numerical examples are presented to illustrate the relationships between fixation times and the probability that an individual adopts the imitation update rule, as well as between fixation times and selection intensity. From the simulated analysis, we find that the fixation time for a mixed process is greater than that of the Moran process, but is less than that of the imitation process. Moreover, the fixation times for a cooperator in the proposed process increase as the probability of adopting an imitation update increases; however, the relationship becomes more complex than a linear relationship.
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supported in part by the National Natural Science Foundation of China under Grant 61971084 and Grant 62001073in part by the National Natural Science Foundation of Chongqing under Grant cstc2019jcyj-msxmX0208in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University,under Grant 2020D05.
文摘Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.
文摘Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.
文摘This paper reports on a study on the effects of reading-writing integrated tasks on vocabulary learning and explored the differential roles of creative construction and non-creative construction in promoting lexical learning. Participants were 90 first-year English majors, randomly assigned to two experimental groups(continuation and retelling) and one control group, with 30 students in each group. Results showed that the continuation group generated a substantial amount of creative construction and produced significantly more instances of creative imitation than the retelling group. The continuation group outperformed the retelling group for both receptive and productive vocabulary knowledge gain and retention, but differences were only significant in terms of productive vocabulary retention. Finally, productive vocabulary knowledge retention among the continuation group was significantly and positively correlated with creative imitation(meaning creation coupled with language imitation), but not with linguistic alignment per se. As productive vocabulary knowledge constitutes the learner ’s ability to use lexical knowledge to express ideas in dynamic contexts, the findings afforded evidence that creative imitation could be the answer to the fundamental issue of L2 learning(i.e., mapping static language onto dynamic idea expression). The pedagogical implications as well as future research directions are also discussed.
基金National Natural Science Foundation ofChina( No.5 9965 0 0 2)
文摘Environmental comprehensive management system, called “the bionic community”, can be established in imitation of biome, which can transform the wastes generated in a certain field into the raw materials of other field. The establishment of the bionic community includes two aspects, i.e., the matching technique and the management system. The main matching technique is the preparation of composite materials made of various wastes. This new kind of material can be divided into four types: polymer matrix, silicate matrix, metal matrix and carbon matrix(or ceramic matrix). The environmental comprehensive management system is formed by organizing a trans-trades joint-management business entity with the products of composite material made of wastes at the core.
文摘The theory of“imitation”in painting occupies a leading position in western art,which originated from the theory of“imitation”in ancient Greece,and has become one of the art theories affecting the world through the continuous development of later generations.Through the exploration of the source of“imitation”in China and the West,there are some comments on the meaning of“imitation”in Chinese classical painting theory,such as“transfer model writing”and“image form”,which is obvious differences from the west.Traditional Chinese painting is a combination of careful observation of natural things and subjective emotions to express their own aesthetic feelings,and ultimately form a vivid artistic conception.Modern imitation is borrowed from Western imitation.In fact,imitation in traditional painting has its own meaning,which contains Chinese aesthetic thought.“Imitation”aesthetics is unique in traditional Chinese painting and is the most important form of painting art.
文摘This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics.We propose a model-free data-driven inverse reinforcement learning(RL)algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert's states and control inputs.This allows us to address the imitation learning problem without prior knowledge of the expert's system dynamics.To achieve this,we provide a basic model-based algorithm that is built upon RL and inverse optimal control.This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators.Theoretical analysis and simulation examples verify the methods.
文摘Gesture recognition is topical in computer science and aims at interpreting human gestures via mathematical algorithms. Among the numerous applications are physical rehabilitation and imitation games. In this work, we suggest performing human gesture recognition within the context of a serious imitation game, which would aim at improving social interactions with teenagers with autism spectrum disorders. We use an artificial intelligence algorithm to detect the skeleton of the participant, then model the human pose space and describe an imitation learning method using a Gaussian Mixture Model in the Riemannian manifold.
文摘When discussing the roots of Arab theatre, we find ourselves confronting two main streams of thought. The first one, represented by prominent Arab writers like Najib Mahfuz, Abbas A1-Aqqad, M. Badawi, and other critics, rejects the theory that an Arab theatre existed before the mid-19th century. The second stream, represented by prominent scholars like Ali A1-Rai, Ibrahim Hamada and S. Moreh, see modern Arab theatre as part of a continuum, emphasizing of some elements of dramatic manifestations in Arab literary heritage. This paper intends to examine these two streams, their evidences and arguments. Such examination will shed some light on the origin of Arab theatre as a literary genre, and how it was influenced, if any, by Western theatrical heritage. Thus, answering the main question of this paper, whether Arab theatre is original or simply a Western imitation.
文摘This paper describes parody as an effective teaching and learning practice in Advanced English course offered to English major juniors and seniors. The objective of this course is to help heighten students towards a more advanced level of English proficiency, and its main preoccupation is doing intensive analyses of carefully selected texts which amount to well-established classics and are characterized with linguistic complexity. To enhance students＇ learning, we embed in the course a practice of parody, which here refers to the creation of an imitative work of an original written work, usually with an attendant comic effect. Upon the completion of each module, students are assigned the task of parodying part of the text, which involves recasting its overall content while retaining formal framework, thus offering students a means of re-paying homage to the excellence of the text. Writing a parody demands great artistry in shaping a creatively simulative work, in fitting exotic content into a local form, and in transplanting new experiential logic into old textual order. The parodies are then peer-reviewed as well as instructor-reviewed. Close observation and survey show that the students have displayed heightened motivation in engaging themselves in the practice and they have benefited greatly from it. Parody proves a particularly fruitful technique in teaching and learning Advanced English, and may also be useful in teaching English writing, since it entitles students to a very unique mode of savoring and wielding the artistic power of the English language.
文摘The imitation cheese is the product with the cheese characteristics which can meet the specific requirements by using the non-milk-derived protein and fat or the full substitution of the milk protein and the milk fat.This paper introduces the production principles,processes and key control points of the imitation cheese with the soybean as main raw material,summarizes the researches and development of the soybean imitation cheese in recent years,and forecasts the market prospect of the soybean imitation cheese in China.
文摘During the translation theoretically developments, the discussion on the standards of translation have never been stopped in term of different times, different countries, by different scholars and translators. Some theories share the same or similar superficies and theoretical representation, but they are totally different at a deeper level. However, without restricting from language and culture, time and space, they are interlinked inside. It is interrogative to produce a consolidation with subtle differences of theories. This paper introduces and analyzes the ＇imitation＇ and ＇distortion＇ on the bases of the Three Types of Translation of John Dryden and Translation Theory of Mao dun; by comparing their theory to provide references to the further translation study.
文摘Imitation models for computing the environmental water pollution level depending on the intensity of pollution sources created by the author over the years are presented. For this purpose, an additive model of a non-stationary random process is considered. For the modeling of its components, models that consider only dilution and self-purification processes are proposed for waste water and three-dimensional turbulent diffusion equations for river waters, and multidimensional Gaussian Markov series are proposed for modeling the random component. The purpose, the capabilities and the peculiarities of such imitation models are discussed taking into account the peculiarities of the water objects. The modular principle of creating imitation models is proposed to facilitate their development and use.
