An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Research on the "three shells" cooperative support technology of large-section chambers in deep mines

The"three shells"cooperative support technology was proposed herein according to both the large deformation of the rock surrounding large-section chambers in deep mines and the precarious stability of the support structures therein.The development range of the plastic zone in the surrounding rock was controlled by a stress shell to reduce the difficulty of controlling the surrounding rock.Additionally,the residual strength of the rock mass in the plastic zone and the self-bearing capacity of the surrounding rock were improved by a reinforced load-bearing shell.Furthermore,a passive load-bearing shell could restore the triaxial stress state of the surrounding rock on the free surface,reduce the influence of the external environment on the surrounding rock,and reinforce the surrounding rock with the strength of the shell.Reasonable layouts of large-section chambers were determined by analyzing the control effect of the stress shell on the surrounding rock under three kinds of in situ stress fields.The orthogonal test method was applied to reveal the influences of different support parameters in the reinforced loadbearing shell and passive load-bearing shell on the surrounding rock stability.The surrounding rock control effect of the"three shells"collaborative support technology was analyzed through numerical simulation and field monitoring.The results show that the maximum displacement between the roof and floor of the coal preparation chamber in the Xinjulong coal mine was approximately 48 mm,and the maximum displacement between its two sides was approximately 65 mm,indicating that the technology proposed herein could meet the long-term control requirements of the surrounding rock stability for large-section chambers in deep mines.

Deep Unfolding for Cooperative Rate Splitting Multiple Access in Hybrid Satellite Terrestrial Networks

Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

Group Cooperative Learning—Making Learning a Deeper Process

The paper is to explore whether or not group cooperative learning in author’s university can make students learning deeply.In 2004,the Chinese Ministry of Education constituted"College English Teaching Syllabus"(College English Teaching Syllabus,2004,showed in appendix),in which it makes it clear that the properties and objectives of College English teaching are:College English teaching is a teaching system which has the content of English language knowledge,English applied skills,learning strategies,intercultural communication.According to the syllabus,lots of Chinese universities will aim to explore new and effective teaching modes,which will stimulate college English teachers to reflect their traditional teaching methods and make the corresponding improvement inevitably.

Cooperative Caching for Scalable Video Coding Using Value-Decomposed Dimensional Networks

Scalable video coding(SVC)has been widely used in video-on-demand(VOD)service,to efficiently satisfy users’different video quality requirements and dynamically adjust video stream to timevariant wireless channels.Under the 5G network structure,we consider a cooperative caching scheme inside each cluster with SVC to economically utilize the limited caching storage.A novel multi-agent deep reinforcement learning(MADRL)framework is proposed to jointly optimize the video access delay and users’satisfaction,where an aggregation node is introduced helping individual agents to achieve global observations and overall system rewards.Moreover,to cope with the large action space caused by the large number of videos and users,a dimension decomposition method is embedded into the neural network in each agent,which greatly reduce the computational complexity and memory cost of the reinforcement learning.Experimental results show that:1)the proposed value-decomposed dimensional network(VDDN)algorithm achieves an obvious performance gain versus the traditional MADRL;2)the proposed VDDN algorithm can handle an extremely large action space and quickly converge with a low computational complexity.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.

MUTS-Based Cooperative Target Stalking for A Multi-USV System

This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle(multi-USV)system.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,a multi-USV target stalking(MUTS)algorithm is proposed.Firstly,a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm.The advantages of the proposed method are twofold:1)it can reduce the amount of data and shorten training time;2)it can filter out more important data in the experience buffer for training.Secondly,in order to avoid the collisions of USVs during the stalking process,an action constraint method called Safe DDPG is introduced.Finally,the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios.In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks,mission operating scenarios and reward functions are well designed in this paper.The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution.Moreover,compared with some existing algorithms,the newly proposed one can provide a higher convergence speed and a narrower convergence domain.

基于“产学研用”融合的人才培养模式研究与实践——以辽宁工程技术大学机械类专业为例

产教融合、协同育人是应用创新型人才培养战略的重要内容和手段。在实施过程中,紧密围绕高校自身资源和条件,结合研究应用型大学培养目标,深度挖掘基于“产学研用”融合的校企合作人才培养模式和特色,多元化创新其在人才培养中的内涵和外延,促进高校、企业及科研机构的深度融合,发挥企业、大学科技园及孵化器的功能,促进校企双方的共同成长;依托高校教育基金会,探索成立企业-教育基金会“人才培养基金”,通过规范性的项目管理,提升学生科学素养和精准就业能力。通过人才培养模式的研究与实践,使其在振兴东北老工业基地和服务辽西北人才战略中,更好发挥应有作用。

切顶沿空留巷充填体—矸石协同承载机理及控制技术研究

针对深部沿空留巷顶底板移近量大、充填体大变形破坏失稳等问题,采用数值模拟和现场实践相结合的方法,分析了传统沿空留巷和切顶卸压协同承载沿空留巷围岩应力演化规律和变形特征,揭示了切顶沿空留巷充填体—矸石协同承载机理。在此基础上,以朱庄煤矿Ⅲ635工作面沿空留巷为工程背景,提出了一种巷旁充填体—矸石组合结构体协同承载的切顶卸压沿空留巷技术,建立充填体—矸石协同支撑顶板力学模型、推导出顶板挠度方程,并进行工程应用。数值模拟结果表明:采用切顶卸压协同承载沿空留巷技术后,实体煤帮和充填体的垂直应力峰值分别降低了28.3%、44.4%,巷道顶底板移近量降低了58.2%。现场应用结果表明:采用该技术后,顶底板和两帮移近量分别为382.9、253.6 mm,工作面支架平均支撑力下降了45.3%,有效控制了巷道变形。可为类似条件下沿空留巷围岩控制提供参考。

共生理论视角下应用型高校校企合作的现实困境及路径优化

全面深化应用型高校校企合作既是实现应用型高校高质量发展的重要举措,也是服务技能型社会建设的关键路径。应用型高校校企合作是一种以创新要素高效流动为核心,实现资源跨界整合和价值增值的双向投资和合作过程。根据共生理论的分析框架,当前应用型高校在深化校企合作过程中,共生单元价值取向分离,利益本位思想固化;共生模式内生动力不足,主体间合作互动疏离;共生环境支持不力,环境建设支撑度不足;共生界面信息传导受阻,资源要素流动通道不畅。为了进一步促进应用型高校校企深度融合,需完善校企合作共生单元,优化利益主体共生路径;创新校企合作共生模式,优化主体协同合作共生路径;营造校企合作共生环境,大力提升环境支撑力度;打通校企合作共生界面,优化资源要素共生路径。

基于深度学习的认知物联网频谱感知算法研究

针对认知物联网(Internet of Things, IoT)对低信噪比(Signal to Noise Ratio, SNR)的频谱感知性能低下以及传统卷积神经网络(Convolutional Neural Network, CNN)频谱感知方法提取数据特征不充分导致感知性能差等问题,提出了一种改进残差网络——ResNeXt的单节点频谱感知算法,ResNeXt只需要设置少量超参数且高度模块化,将该网络在图像处理上的优势应用在频谱感知问题上,先将接收信号转成二维矩阵并归一灰度化处理,得到灰度图像作为网络的输入。通过训练ResNeXt来提取灰度图像特征,将在线数据输入完成频谱感知。将各个次用户(Secondary User, SU)得到的评分向量矩阵直接用融合中心SoftCombinationNet(SCN)融合获得协作频谱感知结果,有效解决了传统硬融合方法检测性能低、软融合处理复杂等问题。实验结果表明,所提方法在低SNR仍能实现低虚警率、高检测概率,优于传统频谱感知方法。

促进高职学生深度学习的混合式合作学习研究

为进一步深化教育信息技术的应用,推动高职院校教育教学改革,提高人才培养质量,研究构建了促进高职学生深度学习的混合式合作学习框架。依据深度学习要求和高职学生特点,分析混合式合作学习对学生的认知、能力和情感三个维度的促进效果。研究表明,混合式合作学习对高职学生的知识水平和能力水平有显著的促进效应,对思维水平和积极情感体验有明显的提升作用。

基于合作关系的多智能体数据库参数调优模型

高维数据库参数空间中的参数调优是提高数据库性能的难点,现有方法更多关注于如何识别重要参数,在如何有效提高可调参数数量的问题上仍存在不足。针对上述问题,基于低维度映射技术和多智能体(Multi-Agent)强化学习技术,提出基于合作关系的Multi-Agent数据库参数调优(Cooperative Multi-Agent Database Parameter Tuning,CMADPT)模型,CMADPT将数据库参数进行分类调优,极大增加了可调参数的数量;提出基于低维度映射的降维模型(Low Dimensional Mapping Model,LDMM),通过低维的合成参数调优高维的数据库参数。实验结果表明,CMADPT模型有效地扩大了可调参数的数量,比主流方法平均提升1.117%的数据库性能。此外,CMADPT每300次迭代训练平均节省1.32 h,极大地提升了算法的时间性能。

基于深度学习的组合服务推荐

针对服务质量参数波动性与服务计算环境的不确定性问题,将深度神经网络的高维输入与强化学习相结合,解决复杂云计算环境下的动态优化和推荐问题,实现了一个基于三层多智能体架构的群智协同服务推荐模型。从更细粒度的活动级构造服务过程的活动状态迁移模型,分析业务活动的局部QoS评价和全局服务协作度量,解决了大规模服务过程建模中状态转移的时间依赖性问题。提出了一种基于深度学习的服务推荐算法(EDQL-BPR),并设计了基于粒子群算法的Q值更新策略,提高深度神经网络的学习智能体的寻优效率,有效提高了BPaaS服务的推荐质量,实现动态环境下效率和适应性的良好平衡。

小组合作竞争主题教学中学生深度学习的影响因素研究

小组合作竞争主题教学是教学改革的重要创新。研究旨在探究小组合作竞争主题教学中学生深度学习的影响因素。通过问卷调查,从学生、教师、环境和交互四个方面进行分析。研究结果显示学生的动机与自我效能感、团队关系、学习能力、学习氛围、教师的期望与关心、学生的反思与评价等因素对学生深度学习水平有显著影响。针对以上这些影响因素,提出了相应的建议,并通过对影响因素的分析,为小组合作竞争主题教学研究提供了参考和实践基础。

基于多Agent深度强化学习的无人机协作规划方法

人机协作控制是多无人机任务规划的重要方式。考虑多无人机任务环境协同解释和策略控制一致性需求,提出基于多Agent深度强化学习的无人机协作规划方法。依据任务知识和行为状态,构建基于任务分配Agent的任务规划器,生成人机交互的相互依赖关系;设计一种深度学习强化方法,解决群体行为最优策略和协同控制方法,并利用混合主动行为选择机制评估学习策略。实验结果表明:作为人机交互实例,所提方法通过深度强化学习使群体全局联合动作表现较好,学习速度和稳定性均能优于确定性策略梯度方法。同时,在跟随、自主和混合主动3种模式比较下,可以较好地控制无人机飞行路径和任务,为无人机集群任务执行提供了智能决策依据。

考虑智能网联车辆影响的八车道高速公路施工区可变限速控制方法

为提升车联网环境下高速公路施工区交通运行效率及安全水平,提出了一种基于强化学习的可变限速控制方法.选取智能驾驶模型和真车试验模型,分别对传统人工车辆和智能网联车辆的跟驰行为进行建模,构建了以瓶颈下游路段交通流量为效率指标、瓶颈路段速度标准差为安全指标的复合奖励值,利用深度确定性策略梯度算法,分车道动态求解最佳限速值.仿真结果表明,所提可变限速控制方法在不同智能网联车辆渗漏率条件下均能有效提升交通流运行效率和安全水平,且在智能网联车辆渗漏率较低时,提升效果更加显著.当智能网联车辆渗漏率为1.0时,瓶颈下游路段交通流量提升10.1%,瓶颈路段速度标准差均值下降68.9%;当智能网联车辆渗漏率为0时,瓶颈下游路段交通流量提升20.7%,瓶颈路段速度标准差均值下降78.1%.智能网联车辆的引入能够提升至多52.0%的瓶颈下游路段交通流量.

基于机器学习的协作MIMO通信网络信号检测算法研究

针对现代无线通信中多输入多输出(MIMO)系统面临的信号检测挑战,文中提出了一种基于机器学习的协作MIMO通信网络信号检测算法。利用深度学习技术,尤其是卷积神经网络与循环神经网络的融合模型,该研究旨在提高信号检测的精度与效率,降低计算复杂度。算法在基于大规模MIMO系统和动态无线环境的仿真测试中展现出优越性能,特别是在低信噪比条件下。研究结果验证了机器学习方法在复杂通信场景中的有效性,也为未来6G及无线通信技术的发展提供了理论与实践参考。

基于时频融合的深度学习调制识别算法

自动调制识别(Automatic Modulation Recognition,AMR)能够在缺少先验信息的条件下,识别出接收信号的调制类型,在非合作通信中起着至关重要的作用。为提高调制识别的准确率,提出了一种基于时频融合的深度学习调制识别算法。该算法将调制信号的时频图作为网络的输入,使用一维卷积分别提取信号的时频特征,并通过计算时频维度上的权重来突出重要的时频信息,使网络学习到更具区分度的时频特征。为了充分利用时频特征之间的互补性和相关性,使用了基于压缩和激励网络(Squeeze-and-Excitation Network,SENet)的时频特征融合策略。利用该网络对11种调制类型进行识别,实现了最高92.5%的识别准确率;在0 dB以上时,平均识别准确率达到90.87%,优于其他的深度学习算法。