Online teaching and learning practices in Asian universities are different from that in western universities. Western universities give emphasis on student-student interaction for learning. Online courses offered by m...Online teaching and learning practices in Asian universities are different from that in western universities. Western universities give emphasis on student-student interaction for learning. Online courses offered by most Asian universities are a kind of mixed mode that comprised simultaneous face-to-face tutorials and online interaction facility. In this situation most students use the online facility to collect resources and to contact their teachers. The quantity of student-student interaction was sporadic in many courses. So research is needed to improve the situation and create an environment for students where they can learn what peer group interaction is and practice it. This paper has presented a model of teaching and learning online for Asian universities. Possible barriers in teaching and learning situations in Asia and students' abilities have been considered to develop the model.展开更多
近年来,综合能源系统作为一种以多种能源形态和设备相互交互的能源系统方案得到了广泛应用和研究.然而,在面对动态复杂的多能源系统时,传统的优化调度方法往往无法满足其实时性和精准度需求.因此,本文设计了一种软深度确定性策略梯度(So...近年来,综合能源系统作为一种以多种能源形态和设备相互交互的能源系统方案得到了广泛应用和研究.然而,在面对动态复杂的多能源系统时,传统的优化调度方法往往无法满足其实时性和精准度需求.因此,本文设计了一种软深度确定性策略梯度(Soft Deep Deterministic Policy Gradient,Soft-DDPG)算法驱动的综合能源系统优化调度方法,以最小化调度周期内系统总运行成本为目标,建立设备运行综合能效评估模型,再采用Soft-DDPG算法对每个能源设备的能效调度动作进行优化控制.Soft-DDPG算法将softmax算子引入到动作值函数的计算中,有效降低了Q值高估问题.与此同时,该算法在动作选择策略中加入了随机噪声,提高了算法的学习效率.实验结果显示,本文所提出的方法解决了综合能源系统能效调度实时性差、精准度低的瓶颈问题,实现了系统的高效灵活调度,降低了系统的总运行成本.展开更多
The article explores the effectiveness of class report in intensive reading course.This Language teaching method works well in terms of relaxing the classroom atmosphere;training students’ courage to express themselv...The article explores the effectiveness of class report in intensive reading course.This Language teaching method works well in terms of relaxing the classroom atmosphere;training students’ courage to express themselves in front of others,and focus on stimulating their interests to study English.展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
实践教学作为创新人才培养的关键环节起着至关重要的作用,将创新方法融入实践教学成为深化新工科建设的有效途径。文中以TRIZ(theory of inventive problem solving)作为创新方法的主要内容,融入了机器人工程专业实践教学的全流程,在教...实践教学作为创新人才培养的关键环节起着至关重要的作用,将创新方法融入实践教学成为深化新工科建设的有效途径。文中以TRIZ(theory of inventive problem solving)作为创新方法的主要内容,融入了机器人工程专业实践教学的全流程,在教学初始阶段通过开设创新方法基础课“创新思维与TRIZ”讲授其基本理论及工具。在专业知识学习阶段以“专业综合实训”课程作为专创融合案例,详细介绍了将TRIZ融入实训的步骤,引导学生使用相关工具对问题进行分析及求解,并对方案进行评估。同时针对课程评价标准中的创新指标进行细化分析,对学生创新能力的培养形成了有效判断,肯定了将创新方法融入实践教学对于提高学生创新能力的有效性。展开更多
文摘Online teaching and learning practices in Asian universities are different from that in western universities. Western universities give emphasis on student-student interaction for learning. Online courses offered by most Asian universities are a kind of mixed mode that comprised simultaneous face-to-face tutorials and online interaction facility. In this situation most students use the online facility to collect resources and to contact their teachers. The quantity of student-student interaction was sporadic in many courses. So research is needed to improve the situation and create an environment for students where they can learn what peer group interaction is and practice it. This paper has presented a model of teaching and learning online for Asian universities. Possible barriers in teaching and learning situations in Asia and students' abilities have been considered to develop the model.
文摘近年来,综合能源系统作为一种以多种能源形态和设备相互交互的能源系统方案得到了广泛应用和研究.然而,在面对动态复杂的多能源系统时,传统的优化调度方法往往无法满足其实时性和精准度需求.因此,本文设计了一种软深度确定性策略梯度(Soft Deep Deterministic Policy Gradient,Soft-DDPG)算法驱动的综合能源系统优化调度方法,以最小化调度周期内系统总运行成本为目标,建立设备运行综合能效评估模型,再采用Soft-DDPG算法对每个能源设备的能效调度动作进行优化控制.Soft-DDPG算法将softmax算子引入到动作值函数的计算中,有效降低了Q值高估问题.与此同时,该算法在动作选择策略中加入了随机噪声,提高了算法的学习效率.实验结果显示,本文所提出的方法解决了综合能源系统能效调度实时性差、精准度低的瓶颈问题,实现了系统的高效灵活调度,降低了系统的总运行成本.
文摘The article explores the effectiveness of class report in intensive reading course.This Language teaching method works well in terms of relaxing the classroom atmosphere;training students’ courage to express themselves in front of others,and focus on stimulating their interests to study English.
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
文摘实践教学作为创新人才培养的关键环节起着至关重要的作用,将创新方法融入实践教学成为深化新工科建设的有效途径。文中以TRIZ(theory of inventive problem solving)作为创新方法的主要内容,融入了机器人工程专业实践教学的全流程,在教学初始阶段通过开设创新方法基础课“创新思维与TRIZ”讲授其基本理论及工具。在专业知识学习阶段以“专业综合实训”课程作为专创融合案例,详细介绍了将TRIZ融入实训的步骤,引导学生使用相关工具对问题进行分析及求解,并对方案进行评估。同时针对课程评价标准中的创新指标进行细化分析,对学生创新能力的培养形成了有效判断,肯定了将创新方法融入实践教学对于提高学生创新能力的有效性。
基金国家自然科学基金联合基金项目(U21A20485)浙江省高等教育“十四五”本科教育教学改革项目(jg20220019)+3 种基金浙江省产学合作协同育人项目(202018)浙江大学2023年度本科教学创新实践项目重点项目(202309)浙江省基础公益研究计划项目(LGG22F030008)浙江大学第一批AI For Education系列实证教学研究项目(202402)。
