Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the p...Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.展开更多
This paper presents a qualitative study to investigate the dynamics in second language(L2)learning strategies under the guidance of the complexity theory.A group of Chinese undergraduate students studying at an intern...This paper presents a qualitative study to investigate the dynamics in second language(L2)learning strategies under the guidance of the complexity theory.A group of Chinese undergraduate students studying at an international university in Thailand were selected as the research participants.Research instruments include interviews,observations,records of participants’on-line chat and posts,and a research journal.The research findings indicate that the changes in the participants’strategies for learning English exhibit typical features of the complex system.The study will provide implications for probing into the nature of L2 strategy and for applying complexity theory to future researches on L2 strategies.展开更多
With the continuous development of artificial intelligence technology,its application field has gradually expanded.To further apply the deep reinforcement learning technology to the field of dynamic pricing,we build a...With the continuous development of artificial intelligence technology,its application field has gradually expanded.To further apply the deep reinforcement learning technology to the field of dynamic pricing,we build an intelligent dynamic pricing system,introduce the reinforcement learning technology related to dynamic pricing,and introduce existing research on the number of suppliers(single supplier and multiple suppliers),environmental models,and selection algorithms.A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions.The first step is to analyze the pricing strategies of e-commerce platforms in mature markets,analyze the optimal pricing and profits of various enterprises under different strategy combinations,compare different market equilibriums and solve the Nash equilibrium.Then,assuming that all consumers are naive in the market,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations,the subgame refined Nash equilibrium is solved.Finally,assuming that the market includes all experienced consumers,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.展开更多
It is well established that at the university,one forms the critical spirit,the spirit of analysis and the spirit of synthesis.What we advocate is a spirit of evaluation.The process we followed is part of a problemati...It is well established that at the university,one forms the critical spirit,the spirit of analysis and the spirit of synthesis.What we advocate is a spirit of evaluation.The process we followed is part of a problematic of teaching French and especially in didactics of writing.We have implemented an experimental device in our teaching practice.This is the dynamic evaluation.This evaluation allows the measurement of the initial level of achievement of a written production.And also the introduction of elements likely to help the subject to modify his usual strategies involved in the realization of a failed written production.But above all the appreciation of the way new strategies are involved.It is a four-phase experience that lasted a whole year.We first put our sample audience to a pre-test;with them,we determined the teaching objectives;then we set up the training workshops for the dynamic assessment,and finally we closed the process with a final test of measurement and evaluation.Two questionnaires were used and an observation grid.展开更多
To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial populatio...To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.展开更多
Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization(MMO). However, these approaches allocate the same computi...Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization(MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy(DRAS)with reinforcement learning for multimodal multi-objective optimization problems(MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.展开更多
Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(...Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.展开更多
Against a background where language learning/learner strategy(LLS)research was criticized,we would like to bring to the fore a key concept,metacognition,which has not been fully understood in the way that criticisms w...Against a background where language learning/learner strategy(LLS)research was criticized,we would like to bring to the fore a key concept,metacognition,which has not been fully understood in the way that criticisms were levelled against LLS research.We argue that despite the justification for some points,such criticisms are not based on a complete understanding of the theoretical foundations of LLS research,nor on what metacognition entails,especially when these two constructs are related to both the cognitive and sociocultural domains of learning.Exactly because metacognition is undergirded by both cognitive and sociocultural underpinnings,it cannot be treated purely as a cognitive enterprise;instead,it should be conceptualized as a set of complex dynamic systems.We argue that some of the criticisms of LLS research are problematic because of the critics'limited understanding of LLS research.These critics have not pointed out close relationships between LLS research and metacognition.To disperse the confusion caused by such criticisms and to advance the field,we elaborate on a dynamic metacognitive systems perspective on second and foreign language learning,teaching and research.We maintain that thinking metacognitively about metacognition with dual or multiple perspectives is necessary.Doing so will enable us to see the contribution of the dynamic metacognitive systems perspective to enhancing our understanding of second and foreign learning,teaching,and research.展开更多
在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learn...在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.展开更多
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51575528)the Science Foundation of China University of Petroleum,Beijing(No.2462022QEDX011).
文摘Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.
文摘This paper presents a qualitative study to investigate the dynamics in second language(L2)learning strategies under the guidance of the complexity theory.A group of Chinese undergraduate students studying at an international university in Thailand were selected as the research participants.Research instruments include interviews,observations,records of participants’on-line chat and posts,and a research journal.The research findings indicate that the changes in the participants’strategies for learning English exhibit typical features of the complex system.The study will provide implications for probing into the nature of L2 strategy and for applying complexity theory to future researches on L2 strategies.
基金His work is supported by Scientific research planning project of Jilin Provincial Department of education in 2020:Analysis of the impact of industrial upgrading on employment of college students in Jilin Province(No.JJKH20200505JY).
文摘With the continuous development of artificial intelligence technology,its application field has gradually expanded.To further apply the deep reinforcement learning technology to the field of dynamic pricing,we build an intelligent dynamic pricing system,introduce the reinforcement learning technology related to dynamic pricing,and introduce existing research on the number of suppliers(single supplier and multiple suppliers),environmental models,and selection algorithms.A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions.The first step is to analyze the pricing strategies of e-commerce platforms in mature markets,analyze the optimal pricing and profits of various enterprises under different strategy combinations,compare different market equilibriums and solve the Nash equilibrium.Then,assuming that all consumers are naive in the market,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations,the subgame refined Nash equilibrium is solved.Finally,assuming that the market includes all experienced consumers,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.
文摘It is well established that at the university,one forms the critical spirit,the spirit of analysis and the spirit of synthesis.What we advocate is a spirit of evaluation.The process we followed is part of a problematic of teaching French and especially in didactics of writing.We have implemented an experimental device in our teaching practice.This is the dynamic evaluation.This evaluation allows the measurement of the initial level of achievement of a written production.And also the introduction of elements likely to help the subject to modify his usual strategies involved in the realization of a failed written production.But above all the appreciation of the way new strategies are involved.It is a four-phase experience that lasted a whole year.We first put our sample audience to a pre-test;with them,we determined the teaching objectives;then we set up the training workshops for the dynamic assessment,and finally we closed the process with a final test of measurement and evaluation.Two questionnaires were used and an observation grid.
文摘To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.
文摘Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization(MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy(DRAS)with reinforcement learning for multimodal multi-objective optimization problems(MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.
基金sponsored by the Autonomous Region Key R&D Task Special(2022B01008)the National Key R&D Program of China(SQ2022AAA010308-5).
文摘Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.
文摘Against a background where language learning/learner strategy(LLS)research was criticized,we would like to bring to the fore a key concept,metacognition,which has not been fully understood in the way that criticisms were levelled against LLS research.We argue that despite the justification for some points,such criticisms are not based on a complete understanding of the theoretical foundations of LLS research,nor on what metacognition entails,especially when these two constructs are related to both the cognitive and sociocultural domains of learning.Exactly because metacognition is undergirded by both cognitive and sociocultural underpinnings,it cannot be treated purely as a cognitive enterprise;instead,it should be conceptualized as a set of complex dynamic systems.We argue that some of the criticisms of LLS research are problematic because of the critics'limited understanding of LLS research.These critics have not pointed out close relationships between LLS research and metacognition.To disperse the confusion caused by such criticisms and to advance the field,we elaborate on a dynamic metacognitive systems perspective on second and foreign language learning,teaching and research.We maintain that thinking metacognitively about metacognition with dual or multiple perspectives is necessary.Doing so will enable us to see the contribution of the dynamic metacognitive systems perspective to enhancing our understanding of second and foreign learning,teaching,and research.
文摘在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.
