Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.

Labeling Malicious Communication Samples Based on Semi-Supervised Deep Neural Network

The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.

Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.

Pseudo-label based semi-supervised learning in the distributed machine learning framework

With the emergence of various intelligent applications,machine learning technologies face lots of challenges including large-scale models,application oriented real-time dataset and limited capabilities of nodes in practice.Therefore,distributed machine learning(DML) and semi-supervised learning methods which help solve these problems have been addressed in both academia and industry.In this paper,the semi-supervised learning method and the data parallelism DML framework are combined.The pseudo-label based local loss function for each distributed node is studied,and the stochastic gradient descent(SGD) based distributed parameter update principle is derived.A demo that implements the pseudo-label based semi-supervised learning in the DML framework is conducted,and the CIFAR-10 dataset for target classification is used to evaluate the performance.Experimental results confirm the convergence and the accuracy of the model using the pseudo-label based semi-supervised learning in the DML framework.Given the proportion of the pseudo-label dataset is 20%,the accuracy of the model is over 90% when the value of local parameter update steps between two global aggregations is less than 5.Besides,fixing the global aggregations interval to 3,the model converges with acceptable performance degradation when the proportion of the pseudo-label dataset varies from 20% to 80%.

Semi-supervised remote sensing image scene classification with prototype-based consistency

Deep learning significantly improves the accuracy of remote sensing image scene classification,benefiting from the large-scale datasets.However,annotating the remote sensing images is time-consuming and even tough for experts.Deep neural networks trained using a few labeled samples usually generalize less to new unseen images.In this paper,we propose a semi-supervised approach for remote sensing image scene classification based on the prototype-based consistency,by exploring massive unlabeled images.To this end,we,first,propose a feature enhancement module to extract discriminative features.This is achieved by focusing the model on the foreground areas.Then,the prototype-based classifier is introduced to the framework,which is used to acquire consistent feature representations.We conduct a series of experiments on NWPU-RESISC45 and Aerial Image Dataset(AID).Our method improves the State-Of-The-Art(SOTA)method on NWPU-RESISC45 from 92.03%to 93.08%and on AID from 94.25%to 95.24%in terms of accuracy.

多机器人在网格环境约束下的运动策略

针对多智能体在网格环境下的寻路与避障规划问题,提出一种分布式、基于深度强化学习的多机器人避障导航方法。该方法基于最近策略优化算法(PPO)用于离散决策下的改进方法进行训练得到的策略模型,该模型通过每个智能体自身的前序多帧仿真激光雷达距离信息,生成符合预设规范的动作,实现多机器人系统在不同环境中的寻路避障。该模型在训练过程中通过引入密度奖励、距离奖励以及步长惩罚,提高了智能体在场景当中的避障寻路能力,减轻了拥塞、死锁等问题的发生,减少了无效路径生成。实验部分在仿真环境中对模型在随机场景、复杂交互场景、障碍场景多个场景进行实验,证明了该模型相比于集中式规划方法大大降低了规划时间,提高了泛化性和稳定性。通过与其他分布式方法相比,证明了所提到的密度、距离奖励设置对智能体安全快速完成任务具有良好作用,在规划效果上减小了与集中式规划方式的差距。

基于深度Q网络优化运行方式的风电场次同步振荡抑制策略

随着我国新型电力系统的不断发展,电力系统次同步振荡问题凸显,严重影响电网的安全稳定运行,而振荡阻尼水平对风电场次同步振荡具有重要影响。由于系统阻尼随电力系统运行方式变化,提出一种基于深度Q网络优化运行方式的风电场次同步振荡抑制策略。首先,通过时域仿真分析桨距角和串补电容对风电场次同步振荡阻尼的影响,在此基础上建立桨距角调整风机出力、并联电容调整线路串补的次同步振荡联合优化数学模型。其次,将深度Q网络算法应用于系统振荡阻尼优化求解问题,获得风电机组次同步振荡抑制优化策略,并与基于遗传算法求解的次同步振荡抑制结果对比。结果表明,该方法有效降低了振荡幅值,提升了系统的阻尼,验证了该方法的合理性和优越性。

针对浒苔目标检测的全局背景强化的位置蒸馏方法

浒苔检测是目前海洋环境智能监测领域研究的重要课题之一。为了有效解决传统浒苔检测方法存在的训练样本需求大的问题,本文提出了一种全局背景强化的位置蒸馏模型(GBS-LD)。通过引入全局上下文模块和背景蒸馏损失分支,解决了原始位置蒸馏方法在建模背景特征上的不足,在复杂海洋环境下有效提高了浒苔检测系统的稳健性。在浒苔检测数据集中,本文模型具有较高的准确性和实时性,为海洋智能监测提供了重要参考。

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

基于卷积神经网络的HEVC帧内预测算法优化

作为HEVC标准中最基础、最重要的技术之一,帧内预测对实现视频编码的高速、高质量和高压缩率具有重要的作用。文中针对帧内预测复杂性问题进行研究,提出一种基于深度卷积神经网络(CNN)的方法,通过学习来预测CTU的划分,从而减少HEVC帧内编码的复杂性。通过建立一个大规模的CTU划分数据库,并利用CNN的能力学习各种CTU划分模式,能够准确地预测CTU的划分,从而避免了传统的穷举搜索,实现了HEVC编码复杂性的显著降低,提高了编码效率。实验结果表明,提出的方法在测试序列和图像上分别将帧内编码时间减少了62.25%和69.06%,与其他最先进的方法相比,比特率分别仅增加了2.12%和1.13%,达到了优化的目的。

DSNNs:learning transfer from deep neural networks to spiking neural networks

Deep neural networks(DNNs)have drawn great attention as they perform the state-of-the-art results on many tasks.Compared to DNNs,spiking neural networks(SNNs),which are considered as the new generation of neural networks,fail to achieve comparable performance especially on tasks with large problem sizes.Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks.This work proposes a simple but effective way to construct deep spiking neural networks(DSNNs)by transferring the learned ability of DNNs to SNNs.DSNNs achieve comparable accuracy on large networks and complex datasets.

Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs

BACKGROUND Deep learning,a form of artificial intelligence,has shown promising results for interpreting radiographs.In order to develop this niche machine learning(ML)program of interpreting orthopedic radiographs with accuracy,a project named deep learning algorithm for orthopedic radiographs was conceived.In the first phase,the diagnosis of knee osteoarthritis(KOA)as per the standard Kellgren-Lawrence(KL)scale in medical images was conducted using the deep learning algorithm for orthopedic radiographs.AIM To compare efficacy and accuracy of eight different transfer learning deep learning models for detecting the grade of KOA from a radiograph and identify the most appropriate ML-based model for the detecting grade of KOA.METHODS The study was performed on 2068 radiograph exams conducted at the Department of Orthopedic Surgery,Sir HN Reliance Hospital and Research Centre(Mumbai,India)during 2019-2021.Three orthopedic surgeons reviewed these independently,graded them for the severity of KOA as per the KL scale and settled disagreement through a consensus session.Eight models,namely ResNet50,VGG-16,InceptionV3,MobilnetV2,EfficientnetB7,DenseNet201,Xception and NasNetMobile,were used to evaluate the efficacy of ML in accurately classifying radiographs for KOA as per the KL scale.Out of the 2068 images,70%were used initially to train the model,10%were used subsequently to test the model,and 20%were used finally to determine the accuracy of and validate each model.The idea behind transfer learning for KOA grade image classification is that if the existing models are already trained on a large and general dataset,these models will effectively serve as generic models to fulfill the study’s objectives.Finally,in order to benchmark the efficacy,the results of the models were also compared to a first-year orthopedic trainee who independently classified these models according to the KL scale.RESULTS Our network yielded an overall high accuracy for detecting KOA,ranging from 54%to 93%.The most successful of these was the DenseNet model,with accuracy up to 93%;interestingly,it even outperformed the human first-year trainee who had an accuracy of 74%.CONCLUSION The study paves the way for extrapolating the learning using ML to develop an automated KOA classification tool and enable healthcare professionals with better decision-making.

基于双决斗深度Q网络的自动换道决策模型

汽车自动变道需要在保证不发生碰撞的情况下,以尽可能快的速度行驶,规则性地控制不仅对意外情况不具有鲁棒性,而且不能对间隔车道的情况做出反应.针对这些问题,提出了一种基于双决斗深度Q网络(dueling double deep Q-network,D3QN)强化学习模型的自动换道决策模型,该算法对车联网反馈的环境车信息处理之后,通过策略得到动作,执行动作后根据奖励函数对神经网络进行训练,最后通过训练的网络以及强化学习来实现自动换道策略.利用Python搭建的三车道环境以及车辆仿真软件CarMaker进行仿真实验,得到了很好的控制效果,结果验证了本文算法的可行性和有效性.

基于深度学习的页岩扫描电镜图像有机质孔隙识别与比较

将深度学习模型引入地质图像分析中,可以大幅提高工作效率,增加研究定量化程度,开拓图像研究新领域。本文以上扬子鄂西地区下寒武统牛蹄塘组页岩的离子抛光扫描电镜图像为例,通过对图片二值化等预处理后,利用Mask-RCNN、FCN和U-Net 3种深度学习模型对页岩中主要矿物、有机质及孔隙等进行识别,比较运行时间与识别结果的准确度,讨论了不同深度学习模型在地质图像识别和处理过程中的适用性和差异性。并优选效果最优的U-Net模型与JMicroVision、Adobe Photoshop等通用图像处理软件识别结果进行孔隙识别对比。结果显示:FCN模型能够基本识别图像中的主要矿物、有机质与孔隙,但对颜色相近的组分和裂缝识别效果较差;Mask-RCNN模型可识别分割性强的主要矿物,但对分辨率较低的孔隙和裂缝识别效果较差;U-Net模型对主要矿物、有机质及孔隙识别效率大大提高,在页岩地质图像识别方面具有优势。相较于通用图像处理软件,U-Net模型识别速度提高了300多倍。基于深度学习U-Net模型识别结果,研究区牛蹄塘组页岩孔隙结构类型可分为矿物内圆状孔、矿物间随机不规则孔、有机棱角状孔和有机密集微孔。基于足够数量电镜图片识别得到的孔隙结构参数对于实际储层分类评价具有参考价值。本实验为基于深度学习的页岩扫描电镜图像识别与分析提供了范例,对提高地质图像研究工作效率和推进油气智能化具有一定的借鉴意义。

基于HPO-VMD和MISMA-DHKELM的短期光伏功率组合预测

为提高光伏发电功率的预测精度,提出一种优化变分态分解(VMD)、多策略改进黏菌优化算法(MISMA)和深度混合核极限学习机(DHKELM)的短期光伏功率组合预测方法。首先,利用VMD分解技术将不同天气类型的功率数据分解成多个模态分量,为避免模态分量间的频率混淆,使用狩猎者(HPO)算法优化VMD的关键参数-分解层数和惩罚因子;然后,针对不同天气类型分解的各分量建立DHKELM预测模型,并采用MISMA优化DHKELM模型的超参数;最后,将各模态分量预测结果求和重构作为最终预测结果。利用新疆某光伏电站的实际数据进行实验分析,实验结果表明:该方法在不同天气类型下均能实现较好的预测效果,预测精度明显优于单一预测模型,与其他方法对比,验证了该方法的有效性。

一种基于改进Faster RCNN的通信网光交箱防尘帽智能检测方法

光交箱防尘帽的检测对于通信网络的正常运行具有重要作用。提出了一种基于改进Faster RCNN(Region Convolutional Neural Network)的通信网光交箱防尘帽智能检测方法。首先,对输入图片进行去噪等预处理,通过残差网络(Residual Netwok,ResNet)进行特征提取,并通过区域生成网络(Region Proposal Network,RPN)初步识别出候选区域,然后经过RolAlign进行池化处理,最后经过特征金字塔网络(Feature Pyramid Network,FPN)对光交箱防尘帽进行二次识别。将该方法应用到光交箱防尘帽缺失的智能检测中,取得了很好的效果。

改进的前端轻量级网络工业手套缺陷检测研究

纺织业是我国的重要产业。近年来,人们对纺织品的需求与日俱增。手套作为纺织品的一种,在实际生产中难免出现一些缺陷样品。为了实现更有效率的生产和管理,本文提出了一种改进的轻量型YOLOv5算法来实现纺织手套的缺陷检测,以YOLOv5s网络作为教师模型,先对其进行剪枝,再将剪枝后的模型作为学生模型,在不损失精度的前提下,训练出一个符合前端检测需求的轻量化模型。实验结果表明,压缩后的模型精度能达到0.93,参数量仅为原教师模型的32%,计算量仅为原模型的36%,更加有利于工业上对纺织手套的精益生产和管理,符合嵌入前端的需求。

基于HGCN的序列手势骨架生成方法研究

手势是交流互动中一种重要的非语言媒介,手势序列生成作为手势表达行为建模的重要任务,在手势分类、手势识别和虚拟人手语驱动等场景有大量应用需求。针对序列手势骨架生成问题,提出了基于Hand-Gesture Graph Convolution Neural Network(HGCN)的手势骨架序列生成方法,采用生成对抗训练框架,在图卷积骨架序列生成方法基础上针对手势骨架序列数据特点,提出图卷积的手部特征增强和基于时空位置编码的自注意力结构的改进方法。实验结果表明,提出的HGCN方法相比基准图卷积骨架生成方法在手势骨架序列生成问题中有更好的结果。