Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mob...Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.展开更多
Goal-conditioned reinforcement learning(RL)is an interesting extension of the traditional RL framework,where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail.Reward shapin...Goal-conditioned reinforcement learning(RL)is an interesting extension of the traditional RL framework,where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail.Reward shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning process.Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution,which may fail to provide sufficient information about the ever-changing environment with high complexity.This paper proposes a novel magnetic field-based reward shaping(MFRS)method for goal-conditioned RL tasks with dynamic target and obstacles.Inspired by the physical properties of magnets,we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these magnets.The nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape,thus introducing a more sophisticated magnetic reward compared to the distance-based setting.Further,we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our method.Experiments results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.展开更多
As assessment outcomes provide students with a sense of accomplishment that is boosted by the reward system,learning becomes more effective.This research aims to determine the effects of reward system prior to assessm...As assessment outcomes provide students with a sense of accomplishment that is boosted by the reward system,learning becomes more effective.This research aims to determine the effects of reward system prior to assessment in Mathematics.Quasi-experimental research design was used to examine whether there was a significant difference between the use of reward system and students’level of performance in Mathematics.Through purposive sampling,the respondents of the study involve 80 Grade 9 students belonging to two sections from Gaudencio B.Lontok Memorial Integrated School.Based on similar demographics and pre-test results,control and study group were involved as participants of the study.Data were treated and analyzed accordingly using statistical treatments such as mean and t-test for independent variables.There was a significant finding revealing the advantage of using the reward system compare to the non-reward system in increasing students’level of performance in Mathematics.It is concluded that the use of reward system is effective in improving the assessment outcomes in Mathematics.It is recommended to use reward system for persistent assessment outcomes prior to assessment,to be a reflection of the intended outcomes in Mathematics.展开更多
为有效提高碳排放配额分配的合理性,并且避免年度结算时碳排放量超标导致环境污染加剧问题,提出基于奖惩因子的季节性碳交易机制,以园区综合能源系统(park integrated energy system,PIES)为对象进行低碳经济调度。首先,构建包含能量层...为有效提高碳排放配额分配的合理性,并且避免年度结算时碳排放量超标导致环境污染加剧问题,提出基于奖惩因子的季节性碳交易机制,以园区综合能源系统(park integrated energy system,PIES)为对象进行低碳经济调度。首先,构建包含能量层–碳流层–管理层的综合能源系统(integrated energy system,IES)运行框架,建立电气热多能流供需动态一致性模型;其次,分析系统内“日–季节–年度”碳排放特性,打破传统应用指标法的配额分配方法,采用灰色关联分析法建立碳排放配额分配模型,并基于奖惩阶梯碳价制定季节性碳交易机制;最后,以系统内全寿命周期运行成本及碳交易成本最小为目标,对执行季节性碳交易机制的PIES进行低碳经济调度,分析长时间尺度下季节性储能参与调度的减碳量。搭建IEEE 33节点电网5节点气网7节点热网的PIES,并基于多场景进行算例分析,验证此调度方法能够实现零碳经济运行,保证系统供能可靠性,为建立零碳园区奠定理论基础。展开更多
肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMR...肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMRI-NF)作为一种新型生物反馈技术,已被应用于其他物质成瘾领域的临床研究和治疗中。在食物成瘾肥胖症中,rtfMRI-NF同样具有重塑异常脑功能、改善摄食行为并达到减重效果的潜力。本综述总结了肥胖患者食物成瘾的功能磁共振脑成像模型,探讨应用rtfMRI-NF作为其潜在治疗工具的可行神经靶点,并回顾了rtfMRI-NF在肥胖应用中的最新研究进展,为未来rtfMRI-NF在肥胖中的治疗策略和临床指导提供参考。展开更多
文摘Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.
基金supported in part by the National Natural Science Foundation of China(62006111,62073160)the Natural Science Foundation of Jiangsu Province of China(BK20200330)。
文摘Goal-conditioned reinforcement learning(RL)is an interesting extension of the traditional RL framework,where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail.Reward shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning process.Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution,which may fail to provide sufficient information about the ever-changing environment with high complexity.This paper proposes a novel magnetic field-based reward shaping(MFRS)method for goal-conditioned RL tasks with dynamic target and obstacles.Inspired by the physical properties of magnets,we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these magnets.The nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape,thus introducing a more sophisticated magnetic reward compared to the distance-based setting.Further,we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our method.Experiments results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.
文摘As assessment outcomes provide students with a sense of accomplishment that is boosted by the reward system,learning becomes more effective.This research aims to determine the effects of reward system prior to assessment in Mathematics.Quasi-experimental research design was used to examine whether there was a significant difference between the use of reward system and students’level of performance in Mathematics.Through purposive sampling,the respondents of the study involve 80 Grade 9 students belonging to two sections from Gaudencio B.Lontok Memorial Integrated School.Based on similar demographics and pre-test results,control and study group were involved as participants of the study.Data were treated and analyzed accordingly using statistical treatments such as mean and t-test for independent variables.There was a significant finding revealing the advantage of using the reward system compare to the non-reward system in increasing students’level of performance in Mathematics.It is concluded that the use of reward system is effective in improving the assessment outcomes in Mathematics.It is recommended to use reward system for persistent assessment outcomes prior to assessment,to be a reflection of the intended outcomes in Mathematics.
文摘为有效提高碳排放配额分配的合理性,并且避免年度结算时碳排放量超标导致环境污染加剧问题,提出基于奖惩因子的季节性碳交易机制,以园区综合能源系统(park integrated energy system,PIES)为对象进行低碳经济调度。首先,构建包含能量层–碳流层–管理层的综合能源系统(integrated energy system,IES)运行框架,建立电气热多能流供需动态一致性模型;其次,分析系统内“日–季节–年度”碳排放特性,打破传统应用指标法的配额分配方法,采用灰色关联分析法建立碳排放配额分配模型,并基于奖惩阶梯碳价制定季节性碳交易机制;最后,以系统内全寿命周期运行成本及碳交易成本最小为目标,对执行季节性碳交易机制的PIES进行低碳经济调度,分析长时间尺度下季节性储能参与调度的减碳量。搭建IEEE 33节点电网5节点气网7节点热网的PIES,并基于多场景进行算例分析,验证此调度方法能够实现零碳经济运行,保证系统供能可靠性,为建立零碳园区奠定理论基础。
文摘肥胖症及减重后不能维持健康体质量的核心因素多为食物成瘾,食物成瘾在神经影像学中表现为奖赏网络与认知控制网络间神经环路的失衡。实时功能磁共振成像神经反馈(real time functional magnetic resonance imaging neurofeedback,rtfMRI-NF)作为一种新型生物反馈技术,已被应用于其他物质成瘾领域的临床研究和治疗中。在食物成瘾肥胖症中,rtfMRI-NF同样具有重塑异常脑功能、改善摄食行为并达到减重效果的潜力。本综述总结了肥胖患者食物成瘾的功能磁共振脑成像模型,探讨应用rtfMRI-NF作为其潜在治疗工具的可行神经靶点,并回顾了rtfMRI-NF在肥胖应用中的最新研究进展,为未来rtfMRI-NF在肥胖中的治疗策略和临床指导提供参考。
