Deep learning algorithm featuring continuous learning for modulation classifications in wireless networks

Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.

Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes

Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

Relational Reinforcement Learning with Continuous Actions by Combining Behavioural Cloning and Locally Weighted Regression

Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Behavioural Cloning, i.e., traces provided by the user;to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real service robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.

无负采样的正样本增强图对比学习推荐方法PAGCL

对比学习(CL)因能够提取数据本身包含的监督信号而被广泛应用于推荐任务。最近的研究表明,CL在推荐方面的成功依赖于对比损失——互信息噪声对比估计(InfoNCE)损失带来的节点分布的均匀性。此外,另一项研究证明贝叶斯个性化排序(BPR)损失的正项与负项分别带来的对齐性和均匀性有助于提高推荐性能。由于在CL框架中对比损失能够带来比BPR负项更强的均匀性,BPR负项存在的必要性值得商榷。实验分析表明在对比框架中BPR的负项是不必要的,并基于这一观察提出了无需负采样的联合优化损失,可应用于经典的CL方法并达到相同或更高的性能。此外,与专注于提高均匀性的研究不同,为进一步加强对齐性,提出一种新颖的正样本增强的图对比学习方法(PAGCL),该方法使用随机正样本在节点表示层面进行扰动。在多个基准数据集上的实验结果表明,PAGCL在召回率及归一化折损累积增益(NDCG)这两个常用指标上均优于SOTA方法自监督图学习(SGL)、简单图对比学习(SimGCL)等,且相较于基模型轻量化图卷积(LightGCN)的NDCG@20提升最大可达17.6%。

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

The Application of Cooperative Learning in the Oral English Classroom

外语教学的最终目标是培养学生用外语进行交际的能力。本文就"共同参与式教学法"的基本理论,探讨了"共同参与式"教学模式在英语口语课课堂的运用,指出口语课内容应符合学生的兴趣和成长需求,教学模式要以学生为中心,在培养学生运用英语进行交流的同时,也要注重培养学生的组织材料的能力、交际能力和独立思考能力。

Control Task for Reinforcement Learning with Known Optimal Solution for Discrete and Continuous Actions

The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for comparison of RL solution methods based on their value functions. In order to solve the control task for continuous actions, a simple idea for generalising them by means of feature vectors is presented. The resulting algorithm is applied using different choices of feature calculations. For comparing their performance a simple measure is

Effectiveness of Cooperative Learning in College English Reading Class

Cooperative learning emerging as the leading new approach to classroom instruction abroad over the past decades has been studied by many researchers from all aspects.This paper mainly focuses on the basics of cooperative learning and tries to answer the question that if the use of cooperative learning produce higher achievement than the traditional methods in college English reading class through experimental study.The analysis contributes to better college English teaching and learning.A conclusion is drawn that cooperative learning is very effective in improving college students reading ability.

Practical Activities to Improve Transfer of Learning in the Nuclear-Related Continuing Professional Educations for Developing Countries in Korea

Much in-class education and training for developing countries have focused on how a learner absorbs knowledge and skills efficiently or effectively in the class,but are less interested in how the learners should transfer the knowledge and skills into their jobs in their workplace.In principle,in-class education and training have a difficulty with applying the learned knowledge and skills to learners’jobs in the workplace in comparison with any other practical-basis training.To overcome this difficulty,many educational stakeholders in the nuclear field have concentrated on how learners can transfer the knowledge and skills absorbed in the class into their jobs in their workplace.The action learning activity for learners can be one of the solutions to apply the knowledge and skills to their job in the workplace.The purpose of this study is to clarify how the transfer of learning has been implemented in the nuclear-related continuing professional educations and training for developing countries in Korea.To accomplish this purpose,this study is implemented as follows.The first is to define the concept of the“transfer of learning”clearly.The second is to clarify the core elements of the transfer of learning.Along with the clarification,the third is to show how the transfer of learning has been implemented in the continuing professional nuclear-related education and training for developing countries in Korea.The fourth is to present core problems in such education and training.As the fifth,this study suggests alternatives to overcome the core problems in the nuclear-related continuing professional education and training.

Continuous Variable Quantum MNIST Classifiers—Classical-Quantum Hybrid Quantum Neural Networks

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

麻醉科进修医师临床教学培训模式的探索研究

目的建立麻醉科进修医师临床教学培训模式,并评价其实施效果。方法选取2023年3—9月在北京协和医院麻醉科进修学习的25名医师为研究对象,在其进修期间实施“导师制⁃知识更新⁃临床实践”的临床教学培训模式。于进修学习前、后分别进行测试(包括基础知识、临床思维和沟通技巧),比较进修医师两次测试的答题成绩,评价培训模式的有效性。同时,收集进修医师对教学培训模式的满意度评价。结果进修学习前、后试卷的有效回收率分别为84%和100%;学员进修学习后的平均成绩显著高于进修学习前[(10.1±1.1)分比(5.6±1.8)分,P<0.01];92%的学员给予了积极反馈。结论“导师制⁃知识更新⁃临床实践”的临床教学培训模式可有效提升进修医师的基础知识、临床思维和沟通技巧,学员满意度较高,具有一定应用价值。

结合Bootstrapped探索方法的CCLF算法

深度强化学习因其可用于从高维的图像中提取出有效信息,从而可以自动生成解决各类复杂任务的有效策略,如游戏AI,机器人控制和自动驾驶等.然而,由于任务环境的复杂性以及智能体低下的探索效率,使得即使执行相对简单的任务,智能体仍需要与环境进行大量交互.因此,本文提出一种结合Bootstrapped探索方法的CCLF算法—Bootstrapped CCLF,该算法通过actor网络中多个head来产生更多不同的潜在动作,从而能够访问到更多不同的状态,提高智能体的探索效率,进而加快收敛过程.实验结果表明,该算法在DeepMind Control环境中具有比原算法更好的性能以及稳定性,证明了该算法的有效性.

避免近期偏好的自学习掩码分区增量学习

遗忘是人工神经网络在增量学习中的最大问题,被称为“灾难性遗忘”.而人类可以持续地获取新知识,并能保存大部分经常用到的旧知识.人类的这种能持续“增量学习”而很少遗忘是与人脑具有分区学习结构和记忆回放能力相关的.为模拟人脑的这种结构和能力,提出一种“避免近期偏好的自学习掩码分区增量学习方法”简称ASPIL.它包含“区域隔离”和“区域集成”两阶段,二者交替迭代实现持续的增量学习.首先,提出“BN稀疏区域隔离”方法,将新的学习过程与现有知识隔离,避免干扰现有知识;对于“区域集成”,提出自学习掩码(SLM)和双分支融合(GBF)方法.其中SLM准确提取新知识,并提高网络对新知识的适应性,而GBF将新旧知识融合,以达到建立统一的、高精度的认知的目的;训练时,为确保进一步兼顾旧知识,避免对新知识的偏好,提出间隔损失正则项来避免“近期偏好”问题.为评估以上所提出方法的效用,在增量学习标准数据集CIFAR-100和miniImageNet上系统地进行消融实验,并与最新的一系列知名方法进行比较.实验结果表明,所提方法提高了人工神经网络的记忆能力,与最新知名方法相比识别率平均提升5.27%以上.

考虑碾压参数影响的压实质量连续检测结果修正研究

为降低压路机行驶速度、行驶方向、振动频率等碾压参数引起的压实质量连续检测结果CMV误差,以动态变形模量E_(vd)为修正目标,基于极限梯度提升XGBoost算法和多元线性回归方法分别建立CMV修正模型,对比修正模型的适用性,并分析各碾压参数对CMV误差的影响。研究表明,XGBoost修正结果与E_(vd)的相关系数高达0.8且误差仅为1.7%,而多元线性回归修正结果与E_(vd)的相关系数仅为0.65且误差高达9.2%,XGBoost修正模型更适用于降低由碾压参数引起的CMV检测误差。高速行驶会造成CMV存在显著的负误差,行驶方向的不同使CMV产生大小相当但正负相反的误差,振动频率对CMV误差的影响是非线性的且与频率的取值有关。最后根据现场压实检测数据的修正结果验证了XGBoost修正模型对降低CMV检测误差的可靠性。

基于网络学习空间的高等学历继续教育PBL教学模式探究

网络学习空间现已成为开展数据支持下个性化教与学的重要入口,但目前绝大多数网络学习空间设计框架并未结合具体的教学模式。我国高等学历继续教育传统教学模式存在教师教学力量薄弱、成人学生学习困难、成人配套教材匮乏、课程教学形式僵化以及评分机制效率低下等若干问题。基于此,应搭建基于网络学习空间PBL教学模式的应用框架,在网络学习空间中完成组织小组,学情分析;创设情景,设计问题;确定目标,制定方案;实践探究,形成结论;小组交流,成果展示;学生总结,教师反馈等教学流程,以治愈传统教学模式的旧疾,体现一个平台、两个主体、三个互动和四个功能的教学价值,实现高等学历继续教育教学模式的数字化与理论化。

课程学习指导下的半监督目标检测框架

为了提高伪标签的质量,解决半监督目标检测(SSOD)中的确认偏差问题,并针对现有算法中忽视无标注数据复杂性导致错误伪标签的难点,提出一种课程学习(CL)指导下的SSOD框架,该框架主要由ICSD(IoUConfidence-Standard-Deviation)难度测量器和BP(Batch-Package)训练调度器这2个模块组成。其中,ICSD难度测量器综合考虑了伪边界框之间的交并比(IoU)、置信度、类别标签等信息,并引入C_IOU(Checkpoint_IOU)方法评估无标注数据的可靠性;BP训练调度器设计2种高效调度策略,分别从Batch和Package角度出发,优先选择可靠性指标高的无标记数据,实现以CL的方式充分利用整个无标记数据集。在Pascal VOC和MS-COCO数据集上的广泛对比实验结果表明,所提框架不仅适用于现有的SSOD算法,而且检测精度和稳定性都得到显著提升。